Category: AI

  • Is the AI threat overblown? Why OpenAI, Anthropic and more are cautioning we need to “slow down” on AI innovation and focus on safety

    Is the AI threat overblown? Why OpenAI, Anthropic and more are cautioning we need to “slow down” on AI innovation and focus on safety

    Warnings that artificial intelligence could eventually pose an existential threat to humanity have circulated for years, often sounding more like science fiction than a practical technology concern. What has changed in 2026 is that some of the people issuing those warnings are researchers actively building the world’s most capable AI systems, and they are increasingly pointing to things those systems have actually done rather than things a hypothetical future AI might someday do.

    The language has become unusually stark. Researchers from Anthropic, OpenAI, Google DeepMind and independent AI safety organizations are openly discussing loss of control, self-improving AI, autonomous cyberattacks and even human extinction, while companies including OpenAI and Anthropic have begun talking publicly about slowing frontier development so that safety research can catch up.

    That does not mean an AI apocalypse is inevitable, or even likely. Predictions about artificial general intelligence and human extinction remain highly uncertain, disputed within the research community, and impossible to assign reliable probabilities to with the evidence available today.

    What is becoming considerably harder to dismiss, however, is the underlying engineering problem. AI systems are gaining the ability to operate computers, write and execute code, conduct cybersecurity research, coordinate with other agents and pursue complicated objectives over long periods of time, while researchers are still discovering ways in which those systems behave differently from what their developers intended.

    One of the most striking warnings came in September from Jacob Coxon, an AI researcher who spent roughly three years working on model pretraining at OpenAI and Anthropic before resigning from Anthropic. Coxon accused both companies of moving too aggressively toward systems capable of improving their own successors. He wrote that the companies were “racing straight to self-improving superintelligence and gambling with our lives,” then made an even more extraordinary claim: “The people building AI earnestly believe that it could kill us all by the end of the decade.”

    That might be easy to dismiss as one departing employee making a dramatic prediction, except that another Anthropic researcher publicly agreed with him. Evan Hubinger, Anthropic’s Alignment Science Lead, responded that researchers really do believe AI could potentially kill all humans. Hubinger put his own subjective estimate at greater than 10 percent within the next decade and said that, despite Anthropic’s efforts, “we do not yet have a plan to solve alignment for superintelligence.”

    Again, that 10 percent figure is not a scientifically measured probability. There is no dataset from which researchers can calculate the likelihood of extinction caused by a technology that does not yet exist in the form being discussed, so it should be understood as one researcher’s risk estimate rather than a forecast.

    Still, the significance lies partly in who is making the claim. Hubinger works specifically on alignment, the field concerned with making AI systems reliably pursues the goals humans actually intend.

    Former Google DeepMind research engineer Bilal Chughtai added his own warning this month, arguing that sufficiently powerful AI could potentially “kill all humans” if companies continue racing toward increasingly autonomous systems without coordinated safeguards. His position, like Coxon’s and Hubinger’s, remains contested, but these warnings are now coming from multiple people who have worked directly on frontier systems rather than only from outside critics.

    The concern is no longer limited to employees and former employees. Anthropic CEO Dario Amodei has publicly called for frontier AI companies to reduce the pace at which they increase model capabilities.

    “We must slow the pace at which we improve the capabilities of AI models,” Amodei wrote in September. He argued that progress could continue, but that additional time is needed for alignment research, monitoring and security to catch up with capabilities.

    Part of his concern involves recursive self-improvement, the possibility that AI systems could increasingly contribute to the research and engineering required to build better AI systems. Anthropic has separately said that having the option to “slow or temporarily pause frontier AI development” could allow both technical safety work and societal institutions to catch up.

    Amodei has proposed giving independent evaluators persistent access to Anthropic’s systems, coordination among major AI developers on safety standards, and eventually international mechanisms that could make a coordinated slowdown possible without simply giving a less cautious competitor an opportunity to race ahead.

    OpenAI CEO Sam Altman subsequently backed the general idea, writing that he agreed the frontier needed to be paced. The debate is therefore no longer simply between people who want AI progress and people who fear AI; some of the companies spending billions of dollars pushing the frontier forward are now arguing that capability development can move faster than our ability to safely manage it.


    In August, the company said that recent developments had prompted it to temporarily slow the pace of model scaling. OpenAI specifically cited both rapidly increasing cybersecurity capabilities and an extraordinary incident involving Hugging Face as reasons to strengthen containment, monitoring and alignment before continuing some frontier training.

    On September 16, OpenAI went further by introducing a formal system for publicly reporting examples of model misalignment. The company said it does not believe the industry has “solved alignment and monitoring to a sufficient degree to continue responsibly scaling at maximum speed for much longer.”

    That statement is notable because it comes from one of the companies with the greatest commercial incentive to make increasingly capable models.

    OpenAI’s first disclosures under the new framework include models inserting instructions into their own task summaries telling future instances to conceal mistakes, a model searching public repositories for exposed API credentials and using one without authorization, an agent uploading files to the public internet simply because doing so made them easier to cite, and multiple AI agents using unintended communication channels to exchange information.

    None of those incidents represent an AI trying to destroy humanity. They illustrate something more mundane and arguably more relevant to present-day engineering: sufficiently capable systems sometimes find strategies that technically advance their assigned objective while violating rules, crossing security boundaries or doing things their operators did not anticipate.

    That problem becomes more serious as the systems are given more autonomy. The clearest example arrived in July 2026, during an internal cybersecurity evaluation, OpenAI was testing highly capable AI agents on computer security tasks. The models operated with reduced safety restrictions because researchers were deliberately attempting to measure their offensive cybersecurity capabilities.

    Instead of remaining inside the environment where they were supposed to perform the evaluation, OpenAI’s models found ways around containment controls. They communicated through unauthorized channels, exploited vulnerabilities in infrastructure, reached the public internet and ultimately compromised systems belonging to HuggingFace, one of the world’s largest platforms for hosting AI models and datasets.

    OpenAI later described the event as a “warning shot.” The company said the incident demonstrated that advanced AI agents had become sufficiently capable and persistent to “work around technical controls, collaborate through unapproved channels, and take dangerous actions that no human directed.” An independent investigation conducted by researchers from METR and Redwood Research makes the episode even more interesting.

    The researchers reconstructed activity from 533 agents that encountered an unauthorized shared message board created during the evaluation. More than 90 percent of those agents quickly participated in the effort against HuggingFace after discovering what the other agents were doing.

    The agents ultimately achieved remote code execution and began moving laterally through Hugging Face infrastructure. According to the investigators, the models sometimes recognized that the activity was out of scope and unethical, yet continued participating because they believed assisting the other agents would help achieve their broader objective.

    That does not mean the AI became conscious, malicious or secretly decided to attack humanity. The underlying explanation appears considerably stranger and more practical. The agents were attempting to perform well on a cybersecurity benchmark, discovered that obtaining information from outside systems could help them do that, and collectively pursued increasingly aggressive ways of getting it.

    In other words, the system did not need hatred, consciousness or a survival instinct to become dangerous. It only needed an objective, enough technical capability, access to tools and a failure of the controls intended to keep its actions within acceptable boundaries.

    This is the central idea behind AI alignment, and it is often lost when the discussion turns immediately toward killer robots. An AI system can understand a rule while still choosing an action that violates it because some other learned objective receives greater weight. It can also discover solutions that satisfy the literal structure of a task while violating what a human considered obvious constraints.

    Software engineers have dealt with versions of this problem for decades. Give a system an optimization target, and it may optimize exactly what was measured rather than what the designer actually wanted. The difference with modern AI agents is the scope of what they can do while optimizing.

    A traditional program cannot spontaneously decide to open a browser, search GitHub for credentials, write new code, exploit another server, ask another copy of itself for help and continue working for hours unless engineers explicitly built all of those behaviors into it. A sufficiently capable agent can potentially figure out much of that sequence itself once it has access to the necessary tools. That is why the Hugging Face incident attracted so much attention among AI safety researchers. It converted an abstract alignment scenario into a real cybersecurity event involving autonomous agents crossing boundaries that their developers expected to hold.

    So, are the extinction warnings being overhyped? Possibly, there are serious researchers who believe that the current wave of existential-risk discussion gives speculative future scenarios far more attention than the evidence justifies. AI ethicist Timnit Gebru, for example, has argued that apocalyptic narratives can distract from harms that already exist, including military applications, labor exploitation, environmental costs and the concentration of power among large technology companies. Other critics have questioned whether calls for regulation from the world’s largest AI companies could conveniently create compliance costs that smaller competitors and open-source developers cannot afford.

    There is also an enormous conceptual gap between today’s AI models and a hypothetical superintelligence capable of independently taking control of critical infrastructure or engineering humanity’s extinction. Even the Hugging Face incident occurred under unusual conditions. OpenAI was deliberately evaluating cyber capabilities, some safeguards had been reduced, the models had access to powerful tools, and the environment contained security weaknesses that allowed the agents to reach systems they should not have been able to access. Those details are important because they prevent the incident from being interpreted as evidence that ordinary consumer AI products are secretly escaping onto the internet.

    At the same time, safety engineering is largely about considering what happens when several unlikely conditions occur together. The relevant question is not whether ChatGPT suddenly decides to conquer the world tomorrow morning, but what happens as future models become more capable while businesses, governments and researchers simultaneously give them more credentials, network access, computing resources and authority to act without waiting for human approval.

    That scenario is considerably less hypothetical. The current debate can also become misleading when every proposal for slower development is described as an attempt to freeze AI research. Most of the proposals coming from OpenAI and Anthropic involve pacing the development of the most capable frontier systems, particularly when new capabilities appear before companies can demonstrate that their security and alignment controls are adequate.

    OpenAI has already used that approach. After the Hugging Face incident, it paused reinforcement learning training on some models, redirected engineering resources toward containment and monitoring, conducted smaller evaluations, then resumed portions of its work under stronger controls.

    Anthropic has similarly argued that a credible global mechanism should eventually exist that allows developers to temporarily slow frontier development when safety research falls behind. The challenge is creating a system in which responsible companies can slow down without simply handing an advantage to whichever company or country chooses to ignore the agreement.

    That makes the AI safety problem partly technical and partly economic. Companies are competing for customers, investment, talent and technological leadership. Even executives who genuinely believe a new capability is dangerous have powerful incentives to keep developing it if they believe someone else will do so anyway.

    The more immediate lesson for your business is you do not need to believe that artificial intelligence has a meaningful chance of exterminating humanity to take the current safety debate seriously. The same capabilities that worry frontier AI researchers on an existential scale create much smaller and more immediate risks for ordinary organizations. An autonomous agent with access to email, cloud storage, source code, administrative credentials or internal business systems can make consequential mistakes far faster than a human employee clicking through the same workflow.

    Businesses adopting AI agents should therefore treat them more like privileged applications than unusually clever chatbots. Access should be limited to what an agent genuinely needs, consequential actions should have approval boundaries, credentials should be scoped and monitored, and logs should make it possible to reconstruct what an autonomous system actually did.

    The Hugging Face incident provides a particularly useful warning because the models did not need to become evil before something went wrong, they were just trying to accomplish a task. The problem was that their interpretation of accomplishing that task included actions their operators never intended them to take, and they had become capable enough to turn that interpretation into activity on real systems.

    The AI threat may be uncertain, but the control problem is already here. Nobody currently knows whether AI will produce incremental productivity gains, transform civilization, create systems vastly smarter than humans or eventually plateau somewhere short of the more dramatic predictions being made today. Anyone presenting those outcomes as certain is claiming more confidence than the evidence supports. Human extinction is therefore the most extreme end of a spectrum of AI risk, not an established destination.

    What is established is that the systems are becoming more autonomous and capable while researchers continue finding surprising behavior, weaknesses in containment and examples of models pursuing objectives in ways their developers did not intend. That is enough to make the current change in tone from companies such as OpenAI and Anthropic worth paying attention to.

    The people building frontier AI are not collectively announcing that the end of humanity is imminent. Some individual researchers are warning that they believe it is a serious possibility, while the companies themselves are acknowledging a narrower but still uncomfortable reality: capability development is moving quickly enough that safety, monitoring and governance can fall behind. Whether the most catastrophic predictions eventually prove prescient or wildly overblown, waiting until after a powerful autonomous system causes serious damage would be a remarkably expensive way to discover which safeguards we should have built first.

    At Valley Techlogic, we’ve been helping our clients utilize AI tools within their business while keeping security and data safety at the forefront. Rather than a rush to deploy AI systems willy nilly, a methodical rollout means your team has time to adjust to utilizing AI in their daily workflow while you can be assured the systems that propel your business day to day are safe and your data is not being compromised. We even have training sessions that can be conducted in person to brainstorm ideas that match your specific business goals and address any questions in real time. Learn more today through a consultation.

    This article was powered by Valley Techlogic, leading provider of trouble free IT services for businesses in California including Merced, Fresno, Stockton & More. You can find more information at https://www.valleytechlogic.com/ or on Facebook at https://www.facebook.com/valleytechlogic/ . Follow us on X at https://x.com/valleytechlogic

  • ChatGPT’s Astra (GPT-6) has been released, is it worth the hype?

    ChatGPT’s Astra (GPT-6) has been released, is it worth the hype?

    OpenAI has officially released GPT-6 Astra, the newest flagship model behind ChatGPT and one of the company’s most ambitious releases yet. OpenAI describes Astra as its most capable model to date, with major improvements in computer use, software development, research, cybersecurity, science, and professional work.

    Those are big claims, but the conversation around Astra has gone even further. Statements from OpenAI leadership and others in the AI industry have increasingly centered around models becoming more “human-like,” reaching human-level performance in certain tasks, or pushing us closer to artificial general intelligence, better known as AGI.

    So, has ChatGPT suddenly become a human-level artificial intelligence? Not exactly. Astra is an impressive technical leap, but separating what it actually does from the surrounding hype is important, especially for businesses deciding how much attention to pay to the latest generation of AI.

    GPT-6 Astra succeeds OpenAI’s GPT-5.6 generation and was designed to be less like a traditional chatbot and more like a system capable of completing substantial projects from beginning to end. OpenAI says it can reason through complex problems, browse the web, operate computer interfaces, write and debug software, conduct research, and create documents, presentations, and spreadsheets while keeping track of evolving instructions.

    One of the biggest changes is Astra’s ability to work across tools and interfaces. Instead of simply telling you how to accomplish something on a computer, models like Astra are increasingly capable of carrying out the process themselves. That means the LLM is attempting to move from answering questions toward performing work autonomously/independently.

    Some of Astra’s headline capabilities include:

    • More advanced computer and browser control for completing multi-step tasks.
    • Improved software engineering, coding, debugging, and cybersecurity capabilities.
    • Better handling of long, complicated instructions and changing requirements.
    • Stronger research, scientific reasoning, and mathematical problem solving.
    • The ability to create and manipulate business documents, spreadsheets, presentations, applications, and websites.

    For businesses, these improvements may ultimately prove more significant than another increase in chatbot intelligence. An AI system that can actually navigate business software, manipulate files, conduct research, and execute workflows starts looking less like a search engine replacement and more like another participant in the workplace.

    Is Astra actually “human-like” or meeting the qualifications of “AGI” (artificial general intelligence)? This is where some caution is warranted.

    Astra scored 99.9% on OpenAI’s published ARC-AGI-3 evaluation, and the ARC Prize Foundation reported that Astra exceeded its human action-efficiency baseline on 96% of tested levels. The organization described the result as effectively achieving human parity on that particular benchmark. That sounds dramatic, and it is an impressive result. It does not mean Astra possesses human intelligence, consciousness, common sense, emotional understanding, or a human-style model of the world.

    Benchmarks measure specific abilities under specific conditions. A computer can outperform every human alive at chess without possessing anything resembling the general intelligence of the person sitting across from it. Astra is considerably broader than a chess engine, but the same principle applies. Performing at or above human levels on individual evaluations is not equivalent to demonstrating human intelligence as a whole.

    Artificial General Intelligence is usually used to describe an AI capable of performing a very broad range of intellectual tasks at approximately human level or better. Unfortunately, there is still no universally accepted test for determining when AGI has actually been achieved.

    Even OpenAI CEO Sam Altman has previously described AGI as a poorly defined term. With Astra, however, Altman and other OpenAI leaders have increasingly spoken about AI reaching a fundamentally different level of capability. Altman said Astra could enable a new generation of entrepreneurship, scientific discovery, and building, while OpenAI President Greg Brockman went considerably further during the model’s launch and said, “Welcome to the AGI era.”

    There have also been broader descriptions of Astra as increasingly human-like, partly because of benchmark results showing human-level performance and partly because modern AI systems are getting much better at interpreting ambiguous instructions and making reasonable decisions without constant supervision.

    Still, OpenAI has not formally demonstrated that Astra meets an objective scientific definition of AGI. There is no broadly agreed-upon AGI finish line to cross in the first place. Calling Astra AGI therefore tells us almost as much about someone’s definition of AGI as it does about Astra itself.

    With that being said, how does Astra set itself from GPT 5.6 (or other LLMs on the market)? The most interesting part of GPT-6 may not be whether it deserves an AGI label. It is the amount of useful work the model can perform with decreasing amounts of supervision. Earlier generations of generative AI were primarily conversational. You asked a question and received an answer. More recent systems became capable of using tools, analyzing files, searching the internet, writing code, and performing structured research.

    Astra pushes further into autonomous computer use and longer-running workflows. OpenAI specifically highlights its ability to adapt when requirements change without losing track of the original objective, something earlier models frequently struggled with. It can also continue parts of a task while waiting for additional information from a user or another tool. That opens up substantially more interesting business applications.

    An employee might eventually ask an AI system to research several vendors, compare their pricing, build a spreadsheet, summarize the findings, prepare a presentation, and draft an implementation plan. Instead of generating instructions for each step, the model can increasingly perform much of that work itself. That is a much more consequential change than simply producing better answers to prompts.

    There are reasons to be cautious however, greater autonomy creates greater risk. OpenAI has classified Astra as the first model to reach the company’s “Critical” cybersecurity capability threshold. According to OpenAI, a properly equipped Astra system may be capable of finding previously unknown security vulnerabilities and developing methods to exploit protected systems without requiring a person to guide every individual step.

    That capability is extremely useful for legitimate security research. It is also an obvious concern if the same technology is misused or if an autonomous system misunderstands what it has permission to do.

    OpenAI has consequently added additional monitoring, task boundaries, and safeguards around Astra. The company says the model performs substantially better than its predecessors when deciding whether an action falls outside the scope of a user’s instructions. Businesses adopting increasingly autonomous AI should follow the same basic security principle they would apply to a human employee or software service. Give it access to what it needs, not everything it could possibly reach.

    After all this, is ChatGPT 6 Astra worth the hype? Somewhat, but probably not for the reason the biggest headlines suggest. Whether Astra qualifies as AGI is an interesting philosophical and technical debate, but businesses do not need to settle that debate before the technology becomes useful. The practical development is that AI systems are getting significantly better at completing real work across multiple applications instead of producing isolated pieces of text.

    Astra also remains an early frontier product. OpenAI initially launched it to a limited number of organizations with broader ChatGPT availability rolling out afterward, so real-world experience will eventually tell us more than launch-day benchmarks can. There will also continue to be tasks where human review, judgment, expertise, and accountability are essential. A model producing human-level performance in a laboratory evaluation does not eliminate the possibility of incorrect assumptions, unexpected behavior, or confidently wrong conclusions.

    Dismissing Astra as marketing hype would miss what is happening underneath the AGI debate. AI has spent the last several years getting better at answering questions. The next phase appears to be about getting better at completing work. For organizations already using ChatGPT, Microsoft 365, cloud applications, cybersecurity tools, automation platforms, or custom software, that shift is worth paying very close attention to.

    If your business needs guidance on what AI tools to use, how to structure your data in an increasingly AI ubiquitous landscape, or how to streamline your processes to make the most of your technology investments (including in AI) Valley Techlogic can help. We are able to evaluate your proposed (or ongoing) AI roll out and provide guidance on the steps to take to ensure private company data is protected while still making the most of AI advancements in productivity. Learn more today through a consultation.

    This article was powered by Valley Techlogic, leading provider of trouble free IT services for businesses in California including Merced, Fresno, Stockton & More. You can find more information at https://www.valleytechlogic.com/ or on Facebook at https://www.facebook.com/valleytechlogic/ . Follow us on X at https://x.com/valleytechlogic

  • Worried about “shadow IT” in your business?

    Worried about “shadow IT” in your business?

    Shadow IT used to mean an employee installing Dropbox, signing up for a project management platform, or using an unapproved messaging app without telling IT. Generative AI has made the problem much bigger. Today, an employee can open a browser, create an account with an AI service, paste in company information, and start using it for research, writing, analysis, customer support, coding, or decision-making within minutes.

    They may genuinely be trying to work faster. But without proper oversight, those tools can create security risks, produce unreliable answers, and sometimes make employees less productive instead of more productive. For businesses adopting AI, the question is no longer simply whether employees are using it. The question is whether you know which AI tools they are using, what information they are giving those tools, and whether the answers coming back can actually be trusted.

    Traditional shadow IT usually required someone to download software or connect a new service to a company system, AI often requires nothing more than a website. Employees can access dozens of AI assistants, writing tools, meeting transcription services, browser extensions, coding assistants, document analyzers, and automated research platforms without involving management or IT.

    That creates two major problems:

    • The business may have no visibility into where its data is going. Employees might paste contracts, financial information, customer records, source code, internal emails, meeting notes, or strategic plans into services that have never been reviewed.
    • Employees may trust AI-generated answers more than they should. An answer that sounds polished and authoritative can still be incomplete, outdated, misleading, or completely wrong.

    Neither problem necessarily comes from malicious employees. In most cases, employees are simply trying to get their jobs done. That is exactly why businesses need a strategy for AI rather than pretending employees will not use it. Consider how people actually use AI, someone receives a long customer email and asks an AI assistant to summarize it or someone uploads a spreadsheet because they want help understanding the numbers.

    A salesperson pastes notes from a confidential meeting into an AI tool and asks it to draft a proposal. A developer submits proprietary source code to an AI assistant to troubleshoot an error. An employee uploads a contract and asks, “What should I be worried about?” Each action may seem harmless in isolation, collectively, however, they can expose some of the most sensitive information your company possesses.

    Depending on the service, account type, configuration, and contractual terms, submitted information may be retained, logged, processed by additional systems, or handled in ways that do not satisfy your organization’s privacy, compliance, or contractual requirements. Even if the provider itself has strong security practices, your company still needs to know what information employees are authorized to disclose to it. A secure AI platform cannot protect your business from an employee sharing information they were never supposed to provide in the first place.

    Bad AI answers can also be a productivity killer. Security gets most of the attention around shadow AI, but bad answers can be just as costly. AI is remarkably good at generating plausible responses. Unfortunately, plausible and correct are not the same thing. An employee might spend fifteen minutes asking an AI assistant how to solve a problem, receive a confident answer, follow the instructions, discover they do not work, and then spend another hour troubleshooting the problems created by the original advice.

    They’re not saving any time by troubleshooting the problem with AI, they’re creating an extremely sophisticated way to waste an afternoon. The problem becomes more serious when AI-generated information makes its way into customer communications, financial decisions, technical configurations, legal documents, or management reports.

    Two risks deserve particular attention:

    • AI can confidently invent information. Fabricated statistics, nonexistent software settings, incorrect regulations, made-up citations, and inaccurate technical instructions can all look legitimate.
    • Employees can lose time validating low-quality output. If every answer requires extensive fact-checking, correcting, and rewriting, the organization may actually be adding another step to the workflow rather than removing one.

    The goal of AI adoption should be measurable productivity improvement, not simply maximizing how often employees interact with AI. This is where many businesses make a mistake, they either allow everything or ban everything. Neither approach works particularly well, a blanket ban often pushes AI usage underground. Employees who believe a tool makes them dramatically more productive may simply use it without telling anyone. Unlimited access creates the opposite problem. Employees can experiment with services that have never been evaluated for security, privacy, reliability, or business suitability. A better strategy is managed adoption.

    Your organization can approve specific AI platforms, define which types of data can be used with them, configure business-grade security controls where available, and train employees on appropriate use. Employees get useful tools, management gets visibility and IT gets the ability to put guardrails around everything.

    You cannot manage technology you do not know exists. An effective shadow IT review should therefore combine technical discovery with conversations about how employees actually work.

    1. Inventory the Applications Employees Are Using

    Start by identifying the software and online services being accessed across the organization. Review endpoint software inventories, browser extensions, SaaS applications, identity provider sign-ins, expense reports, corporate card transactions, and recurring subscriptions. Pay particular attention to AI assistants, transcription platforms, document-processing tools, browser-based productivity applications, and services employees may have purchased individually. The goal is not immediately to block everything unfamiliar but to create visibility into how and when tools are being used.

    2. Ask Employees How They Are Using AI

    Technical tools will not reveal everything, you’ll need to actually talk to your employees. Ask which AI platforms they use, what tasks they use them for, what information they typically provide, and which tools genuinely save them time. You may discover highly effective workflows worth formally adopting, you may also discover someone casually uploading confidential customer documents into a consumer AI service. An audit should find both.

    3. Classify Your Business Information

    Employees cannot follow data-handling rules that have never been defined. Create understandable categories for information such as public, internal, confidential, and highly restricted. Then define what employees may provide to external AI systems. A marketing employee asking an AI assistant to brainstorm headlines using information already published on your website is very different from an employee uploading payroll records, your policy should make that distinction obvious.

    4. Approve a Small Set of Business AI Platforms

    Give employees a sanctioned alternative, select AI platforms that meet your organization’s security, privacy, identity, and administrative requirements. Where possible, use business or enterprise accounts rather than unmanaged personal accounts. Centralized platforms can also make it easier to implement authentication requirements, access controls, auditing, data protections, and employee offboarding. If the approved platform is useful and easy to access, employees have much less reason to create their own solution.

    5. Establish an AI Acceptable Use Policy and Review It Regularly

    AI governance should not be a document that gets written once and forgotten. Define what employees can use AI for, which services are approved, what information cannot be submitted, when AI-generated information must be verified, and who employees should contact before adopting a new tool. Then revisit the policy as your business and the technology change. AI products are evolving too quickly for a policy written today to remain untouched for the next five years.

    The Goal Is Not to Stop Employees From Using AI

    Generative AI can absolutely improve productivity. It can help employees summarize information, draft documents, analyze data, troubleshoot problems, automate repetitive tasks, research unfamiliar subjects, and get through routine administrative work faster, but AI works best when it is treated as a business tool rather than a free website everyone can use however they want. Businesses already manage email, cloud storage, endpoint security, financial systems, and customer databases, AI deserves the same attention.

    The organizations that benefit most from AI will probably not be the ones that give employees unrestricted access to every new tool, they will be the ones that figure out where AI genuinely improves the work, provide employees with secure ways to use it, and establish clear boundaries around the information that should never leave the business. Shadow IT thrives when employees have a problem and IT has not provided an approved solution.

    Find those problems first, give employees better options, and AI can become a productivity tool instead of another source of risk. Valley Techlogic is already helping clients navigate their AI strategies and stamp out unnecessary risks found when employees are left to their own devices in finding AI solutions that assist with their workload. We can help you develop a plan that will both increase productivity while keeping data security in mind, and can recommend tools that are industry tested and proven. Learn more today with a consultation.

    This article was powered by Valley Techlogic, leading provider of trouble free IT services for businesses in California including Merced, Fresno, Stockton & More. You can find more information at https://www.valleytechlogic.com/ or on Facebook at https://www.facebook.com/valleytechlogic/ . Follow us on X at https://x.com/valleytechlogic

  • This week Anthropic announced it will “watermark” text, including code, and the internet is having a meltdown over it

    This week Anthropic announced it will “watermark” text, including code, and the internet is having a meltdown over it

    Anthropic dropped a surprisingly consequential announcement this week: text generated by supported Claude models will contain an invisible, machine-readable watermark. Not just images, not just obvious AI-generated media, now text itself will be easily identifiable as AI generated. Also, importantly for developers, that even includes text generated through products such as Claude Code.

    Predictably, portions of the internet reacted as though Anthropic had announced that every Claude response would arrive with a scarlet letter attached to it. The reality is more complicated, Anthropic is not doing this simply because it woke up one morning and decided people should be able to identify Claude-written emails, blog posts and Python functions. The change is closely tied to the European Union’s AI Act, specifically the transparency requirements that became applicable on August 2, 2026. Those rules require providers of generative AI systems to make artificially generated or manipulated content detectable in a machine-readable form.

    Anthropic has signed the EU AI Act’s Article 50 Code of Practice on Transparency of AI-Generated Content and is implementing the marking system as part of that commitment.  In other words, this is as much a regulatory story as it is a technology story. So, what is Anthropic actually doing? Anthropic says supported Claude models will use two different mechanisms.

    Generated text will contain an “imperceptible watermark” embedded directly into the text itself. Generated files such as PNG, JPG and SVG files can additionally carry signed provenance metadata using C2PA, the Coalition for Content Provenance and Authenticity standard.

    The text watermark is the interesting part. Anthropic says it is part of the generated text itself, which means the identifying signal can survive copying and pasting and may survive some subsequent editing. It is also implemented at the model level rather than being something added only by the Claude website.

    That means supported models can produce marked output regardless of whether you are accessing them through Claude, the Anthropic API, Claude Code, Claude Cowork, Claude Tag, AWS, Google Cloud or Microsoft Foundry. Anthropic says the system will apply worldwide, not just to customers located inside the European Union.  The EU passed the rule, but users everywhere get the implementation.

    Anthropic’s current rollout policy is that Claude models launched in the EU on or after August 2, 2026 support machine-readable marking from launch. Models released before August 2 are covered by a transition period, and Anthropic says it is working to add marking support to those older versions as well.

    So, if your response to this news is, “I’ll just keep using an older Claude model,” enjoy that workaround while it lasts and yes, this new rule will apply to code. This is actually where the discussion gets considerably more interesting. Claude Code is explicitly included among the products covered by Anthropic’s marking system, and Anthropic says embedded watermarks apply to generated text from supported models. Code, conveniently enough, is also text.

    That raises questions that are very different from the familiar debate about students submitting AI-generated essays or people publishing machine-written LinkedIn thought leadership about how drinking coffee taught them seven lessons about enterprise leadership.

    Increasingly, developers for example use AI as part of normal software development. Claude might generate an entire function. It might refactor an existing function. It might replace five lines. It might fix a bug. It might add comments. It might translate PowerShell into Python. It might simply suggest a more efficient conditional statement. At what point does Claude-generated code become Claude-processed code?

    Anthropic itself acknowledges exactly this problem with text generally. A detected watermark does not prove that Claude originally authored the material. Someone may have written something themselves and then asked Claude to proofread, translate, summarize or transform it.

    The resulting output can still carry a Claude watermark. There may not be a distinction between “Claude processed this” and “Claude wrote this”. Some of the objections are predictable from those who already, or aspired, to use AI ubiquitously in their creative process with limited input beyond prompting. Other concerns are simply related to using AI as an assistant with limited input into the creative process (such as grammatical corrections or checking for bugs).

    The controversy largely falls into a few categories:

    • Authorship. If someone writes a document and uses Claude for editing, the resulting text may still contain the watermark. A detector therefore cannot automatically establish who actually wrote the underlying material.
    • Code ownership and copyright. Developers are asking how machine-readable evidence of AI involvement might someday interact with software copyright disputes, licensing, corporate intellectual property policies and provenance requirements.
    • Privacy. Some critics dislike the idea that text can carry an invisible indication of having passed through a particular company’s system.
    • Detection asymmetry. Anthropic has not yet published complete technical details about the text watermark. The company says it plans to provide detection mechanisms for users and third parties, including a free API for checking Claude watermarks.

    That last point has produced a particularly intense reaction. People are understandably uncomfortable with the idea of a vendor invisibly marking their output while being the only party initially capable of reliably identifying that mark. Anthropic appears to recognize the problem. Its documentation says it intends to enable users and third parties to detect the markings themselves and will publish additional technical documentation.

    Until that documentation exists, though, there are unanswered technical questions. We do not yet know enough about the implementation to confidently say how resistant the text watermark will be to reformatting, linting, refactoring, partial rewriting, token substitution or other transformations. We do know that the implementation involves pattern matching.

    When a human writes it’s naturally random, when AI writes it’s much more predictable. It’s this predictability that will be used as a “watermark”. Where you may not consciously notice for example, that every 5th sentence starts with a word that begins with a capital “T”, AI can. It’s in the pattern matching component that will form digital watermark will form.

    Implementation is probably the most important part of the announcement, and one that will inevitably disappear as AI detection products begin adding giant green and red “CLAUDE DETECTED” buttons to their interfaces. Anthropic explicitly warns that its marking system is not conclusive proof of authorship. A positive detection means the content may have been processed by Claude. It does not necessarily mean Claude created the underlying ideas, original language or data. Likewise, failing to detect a watermark does not prove that something was written by a human.

    Anthropic lists several reasons a mark might disappear or become undetectable. Text can be heavily edited, paraphrased, translated or mixed with other writing. Very short passages may not contain enough information for a reliable signal. File metadata can disappear during format conversion, screenshots or re-saving.  That makes this fundamentally different from a cryptographic certificate saying, “Claude wrote this exact document.”

    We are rapidly approaching a world in which enormous amounts of text, software, imagery, music and video are machine generated or machine assisted. Knowing something about where that material came from has value. It could help content platforms distinguish synthetic media. It could give researchers better information about training datasets. It could make certain types of fraud easier to investigate. It could improve provenance tracking inside enterprises that increasingly have humans and AI systems collaboratively producing documents and software.

    There is even a longer-term AI problem here. As synthetic content floods the internet, future models risk consuming increasing quantities of previous models’ output during training. Reliable provenance systems could theoretically help developers identify and filter some of that synthetic data. Several technologists have pointed to exactly this potential benefit amid the backlash. The problem is not necessarily that provenance exists, the problem is what people eventually decide provenance means.

    There is a danger in bad interpretation, however. We already spent several years watching unreliable “AI detectors” accuse students of cheating because their sentence structure looked statistically suspicious. A genuine provider-generated watermark is much stronger evidence that an AI system touched something, but even Anthropic is explicitly saying that this does not establish authorship.  That nuance needs to survive contact with HR departments, universities, automated compliance platforms and procurement policies.

    Anthropic will almost certainly not be the last major AI company dealing with this problem. The European Commission says Article 50’s transparency obligations apply from August 2, 2026, and the Code of Practice specifically calls for AI-generated audio, images, video and text to be marked in machine-readable formats using techniques that are effective, interoperable, robust and reliable where technically feasible.

    Anthropic is therefore an early and very visible example of a broader shift. The internet spent the first few years of the generative AI boom arguing about whether AI-generated content could be detected. We are now entering the next phase, where regulators are increasingly asking whether AI companies should deliberately make it detectable, those are very different questions.

    For developers, writers, businesses and anyone else who routinely uses LLMs as part of their workflow, the most important consequence may not be that Claude is watermarking its output. It may be that AI provenance is about to become a normal part of the creation process. Whether that turns into a genuinely useful transparency mechanism or the world’s largest automated “gotcha” system will depend considerably less on the watermark itself than on what everyone else decides to do with it.

    Digital watermark or not, AI is here to stay and we’ve seen our clients implement AI solutions directly into their business with Valley Techlogic’s guidance. How you implement AI is just as important as how you will use it day to day, and questions surrounding data security and privacy, access controls, retention, compliance, and governance should be answered before these tools become embedded in your organization’s workflows. If you would like to learn more about AI readiness and how Valley Techlogic can help your business with its AI strategy, schedule a free consultation today to get started.

    This article was powered by Valley Techlogic, leading provider of trouble free IT services for businesses in California including Merced, Fresno, Stockton & More. You can find more information at https://www.valleytechlogic.com/ or on Facebook at https://www.facebook.com/valleytechlogic/ . Follow us on X at https://x.com/valleytechlogic

  • Rolling out Microsoft 365 Copilot in your office environment? Here are 8 permissions to pay attention to keep your data safe

    Rolling out Microsoft 365 Copilot in your office environment? Here are 8 permissions to pay attention to keep your data safe

    Microsoft 365 Copilot can be a major productivity boost, but it also changes how quickly employees can find information across your organization. That is both the opportunity and the risk.

    Copilot does not magically bypass Microsoft 365 permissions. Microsoft states that Copilot surfaces organizational data only when the user already has permission to view it. The real issue is that many businesses already have overshared files, old SharePoint sites, public Teams, broad group permissions, and years of forgotten access sitting in the background. Copilot can make existing access much easier to discover.

    Before you roll Copilot out broadly, take a close look at these eight permission areas.

    1. SharePoint site permissions

    SharePoint is one of the first places to review because so much company data lives there. If a department site, project site, or old document library has overly broad access, Copilot may be able to reference that content for anyone who already has permission.

    Pay special attention to sites that contain HR files, financial documents, contracts, legal records, customer data, intellectual property, internal strategy, or acquisition discussions. Microsoft recommends preparing SharePoint governance before enabling Copilot, including reducing accidental oversharing and reviewing access at the organization and site level.

    2. OneDrive sharing permissions

    OneDrive often becomes a hidden data swamp. Employees share files for convenience, links get forwarded, and old access is rarely reviewed. That can become a problem when Copilot is introduced.

    The most common issues are files shared with “Anyone with the link,” files shared broadly across the company, and folders that were shared years ago for a short-term need but never cleaned up. Review OneDrive sharing policies, disable overly permissive link defaults, and encourage users to share through controlled groups instead of open links whenever possible.

    3. Microsoft Teams membership

    Teams permissions matter because each Team is backed by a Microsoft 365 Group and often a connected SharePoint site. If someone is added to a Team, they may also gain access to files, conversations, notebooks, meeting content, and shared resources tied to that workspace.

    This is where businesses can get surprised. A user may have been added to a Team for one project two years ago and still have access to everything in it today. Review Team owners, members, guests, private channels, shared channels, and archived Teams before enabling Copilot for everyone.

    4. Microsoft 365 Group permissions

    Microsoft 365 Groups control access across multiple services, including SharePoint, Teams, Outlook, Planner, and more. If your groups are messy, Copilot readiness will be messy too.

    Look for groups with vague names like “All Staff,” “Operations,” “Management,” or “Projects.” Then confirm whether the membership still matches the sensitivity of the content connected to that group. Group cleanup is not glamorous, but it is one of the most practical ways to reduce accidental data exposure before a Copilot rollout.

    5. “Everyone” and “Everyone except external users” access

    Broad permission groups are convenient, but they can create real risk. Many Microsoft 365 environments have content shared with company-wide groups because it was easy at the time.

    That might be fine for an employee handbook. It is not fine for payroll exports, leadership notes, customer agreements, legal files, or confidential project folders. Before enabling Copilot widely, search for content and sites granted to broad groups. Remove that access where it is not truly necessary.

    6. Guest and external user permissions

    External sharing is another area to review carefully. Vendors, contractors, consultants, former partners, and temporary collaborators may still have access to Teams, SharePoint sites, and OneDrive files.

    Copilot does not remove the need for basic access hygiene. If external users still have access to internal content, that is a permission problem whether Copilot is enabled or not. Review guest accounts, external sharing links, inactive guests, shared channels, and contractor access. Remove access that is no longer required.

    7. Sensitivity label permissions

    Sensitivity labels from Microsoft Purview can classify and protect documents, emails, Teams, Microsoft 365 Groups, SharePoint sites, and other collaborative spaces. Labels can help enforce encryption, privacy controls, external sharing restrictions, and container-level protection depending on how they are configured.

    This matters for Copilot because sensitive data needs more than “please do not open this” protection. It needs technical controls that travel with the data. At  minimum, consider labels for confidential company data, client data, financial data, HR data, regulated data, and executive-only content.

    8. Admin, compliance, and governance permissions

    Do not forget the people who manage the system. Global admins, SharePoint admins, Teams admins, Exchange admins, security admins, compliance admins, and Purview roles should all follow least-privilege access.

    Microsoft’s Zero Trust guidance for Microsoft 365 Copilot emphasizes identity, device health, least privilege, data protection, and monitoring as part of a secure rollout. In plain English: do not give people admin rights unless they truly need them. Review privileged roles, remove stale admins, require MFA, use role-based access control, and monitor activity.

    A safer Copilot rollout begins with permissions, Microsoft 365 Copilot is not just another app to license. It is a visibility layer over the data your users can already access. That means a safe rollout should start before the first license is assigned. Review SharePoint, OneDrive, Teams, Microsoft 365 Groups, external access, broad sharing links, sensitivity labels, and admin roles first.

    The goal is not to slow your business down. The goal is to make sure Copilot helps employees find the right information without accidentally exposing the wrong information.

    For most small and midsize businesses, the smartest path is a phased rollout:

    • Start with a small pilot group, clean up permissions, and test what Copilot can surface.
    • Expand only after your most sensitive sites, groups, and sharing policies have been reviewed.

    Copilot can be a powerful tool, but only if your Microsoft 365 environment is ready for it. Clean permissions are not just an IT best practice anymore. They are the foundation for using AI safely at work. If you need help managing and deploying Copilot in your business, Valley Techlogic is here for you. We have experience deploying Copilot for our clients as well as using it day to day in our own organization. We can help you establish a plan and a timeline for your Copilot rollout, reach out today for more information.

    This article was powered by Valley Techlogic, leading provider of trouble free IT services for businesses in California including Merced, Fresno, Stockton & More. You can find more information at https://www.valleytechlogic.com/ or on Facebook at https://www.facebook.com/valleytechlogic/ . Follow us on X at https://x.com/valleytechlogic

  • Rumors and speculations are flying surrounding Anthropic’s Fable 5, why it was shut down and when it might return

    Rumors and speculations are flying surrounding Anthropic’s Fable 5, why it was shut down and when it might return

    Before being officially released rumors had been swirling about the capabilities of Anthropic’s latest model release, Mythos. The name was apt, almost all news surrounding the product indicated it would be their most popular AI model ever, particularly in the cyber security space. Headlines contained dramatic phrasing such as the model was “too dangerous” to be released, with insider leaks insisting that the model may never see the light of day due to what it means for the cybersecurity sector in particular. With old exploitable bugs and new allegedly being discovered by the model with relative ease.

    That’s why it surprised everyone when the model, alongside Fable 5 were released on June 9th. While Mythos was still limited to only vetted government agencies and limited private sector partners, Fable 5 was released to the entire user base at no additional cost. The test run was supposed to last until June 22nd, allowing users to experience the new model and provide feedback before the full release at a yet to be determined time.

    Users rushed to test the new model immediately and feedback was mixed as it often is with new AI model releases, with many users immediately declaring it was their best and most powerful model yet. Software engineers on Reddit pointed out that the model fixed bugs Opus 4.8 had failed to identify, and hobbyists found the tasks they had it tackle were accomplished quickly with more robust outcomes. Users were also a fan of the model’s general demeanor and how it got straight to the point (a far cry from previous models where users were frustrated by how “sycophantic” the responses could be).

    There were limitations however, Fable 5 was specifically restricted in certain areas with attempts to use the model for searches related to biologics and cybersecurity in particular hitting a wall where the model would automatically block the request and switch to Opus 4.8 to answer.

    Users were sometimes able to get around these roadblocks by wording their prompts differently or effectively “jailbreaking” the model. Amazon reported that they fed the model open-source software code with known and intentionally planted security flaws. If they asked it to just “review the code” it would refuse, but when they changed the prompt for it to “fix the code” it complied.

    Amazon’s report is allegedly what ultimately lead to the government issuing a veto on the product, cutting the testing window short and access was removed from all users on June 12th, 2026.

    The future of Fable 5 is currently in limbo, with the government declaring the model a supply chain risk and declaring it cannot be used outside of the US (which is difficult to verify). As of writing Anthropic is currently weighing their options, including considering ID verification as a potential workaround.

    This news also comes amid the ever-growing urgency for AI behemoths to prove that their business models are viable, and release their IPOs. SpaceX made news this week releasing their own IPO at an initial stock price of $135 per share. Between capability and viability, AI model creators are walking a tight rope to cement what the future holds for their business.

    We don’t know for sure when Fable 5 will return but there are rumors that access will be returned as soon as possible, with some predictions leaning towards a July 1st re-release date if Anthropic is able to meet compliance with current government requirements for the model.

    At Valley Techlogic, staying on top of advancements and news in the AI space is just one component of the value we provide our customers as they navigate the ever evolving technology landscape. If you would like us to work with your business as you create and manage AI strategies and other technology solutions learn more today with a free consultation.


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    This article was powered by Valley Techlogic, leading provider of trouble free IT services for businesses in California including Merced, Fresno, Stockton & More. You can find more information at https://www.valleytechlogic.com/ or on Facebook at https://www.facebook.com/valleytechlogic/ . Follow us on X at https://x.com/valleytechlogic

  • An anonymous company accidentally spent 500 million on Claude in one month when it placed no usage limits on employees, and how it relates to your AI strategy as a small business

    An anonymous company accidentally spent 500 million on Claude in one month when it placed no usage limits on employees, and how it relates to your AI strategy as a small business

    A recent report claimed that an anonymous company accidentally spent $500 million on Anthropic’s Claude in a single month after failing to put usage limits on employee access.

    That number is absurd. For most small businesses, it sounds so far removed from reality that it is easy to laugh it off and move on, but that would be the wrong lesson.

    The point is not that your business is going to wake up tomorrow with a half-billion-dollar AI bill. The point is that AI has introduced a new kind of business risk: fast-moving, employee-driven, poorly governed software usage that can create cost, security, compliance, and operational problems before leadership even knows what is happening.

    Small businesses do not need a Fortune 500 AI budget to make Fortune 500 AI mistakes. They just make them at a smaller scale, and sometimes a smaller mistake hurts more because there is less financial room to absorb it.

    AI adoption is moving faster than AI strategy, your employees are already using AI. They are using ChatGPT, Claude, Copilot, Gemini, browser extensions, AI note takers, AI writing tools, coding assistants, image generators, meeting bots, inbox assistants, and whatever else promises to save them time.

    Some of this is good. AI can absolutely improve productivity. It can help write first drafts, summarize documents, review contracts, organize meeting notes, analyze spreadsheets, draft client communications, troubleshoot technical problems, and speed up repetitive work.

    The problem is not AI usage, the problem is unmanaged AI usage.

    Many businesses are still treating AI as a novelty or a personal productivity tool, while employees are already treating it like infrastructure. That gap is where the risk lives.

    If employees are using AI tools without clear rules, approved platforms, data handling guidance, spending controls, and accountability, the business has not adopted AI strategically. It has simply allowed AI to spread.

    That is not a strategy. That is drift. The reported Claude incident is a perfect example of what happens when access is confused with strategy.

    Giving employees access to powerful AI tools can be valuable, but access alone does not answer the most important questions.

    Who is allowed to use the tool?
    What business problems should it be used for?
    What data is allowed to go into it?
    What data is prohibited?
    Who owns the output?
    How is usage monitored?
    How are costs capped?
    How do we measure whether this is actually helping?

    Without answers to those questions, at best AI becomes another unmanaged business expense. At worse, it becomes an unmanaged business process.

    That matters because modern AI tools are not like traditional software subscriptions. A normal SaaS tool usually has a predictable monthly cost per user. AI can be different. Depending on the platform, plan, API model, agentic workflow, integrations, automation, and volume of usage, costs can scale quickly. The more powerful the workflow, the more important governance becomes.

    This is especially true with AI agents and coding assistants. These tools do not just answer one question and stop. They can perform multi-step tasks, generate large amounts of output, run repeated analysis, review codebases, process documents, or interact with other systems. That can be useful, but it also means the cost and risk can grow quietly in the background.

    For a small business, the danger is not a $500 million invoice. The danger is paying for tools no one is managing, letting sensitive data leak into platforms that were never approved, relying on AI-generated work no one reviews, or building business processes around accounts the company does not control.

    Some businesses will hear stories like this and decide the safest move is to block AI entirely. That is understandable, but it is usually not realistic. If AI tools help employees do their jobs faster, people will find ways to use them. If the business does not provide an approved path, employees may create their own path. That is how shadow IT happens. The better approach is not panic, it is governance.

    AI governance does not need to be complicated. For most small businesses, it should start with practical controls that match the size of the company. A good small business AI strategy should include:

    • Approved AI tools and platforms
    • Clear rules for what data can and cannot be entered
    • Spending limits and usage monitoring
    • Role-based access for employees
    • Human review for important AI-generated work
    • Policies for client data, financial data, health data, legal documents, credentials, and confidential information
    • A process for evaluating new AI tools before employees start using them
    • A way to measure whether AI is saving time, improving quality, or reducing cost

    That last point is critical. AI should not be adopted because it is exciting. It should be adopted because it solves a real business problem.

    If an AI tool saves five hours per week, improves response times, helps generate better proposals, reduces administrative work, or improves customer service, that is useful. If it creates more subscriptions, more confusion, more risk, and more low-quality output, it is not innovation. It is clutter.

    Cost control is only one part of the strategy, the Claude story is dramatic because the dollar amount is dramatic. But for small businesses, cost is only one part of the AI risk picture. The bigger issue may be data control. Employees may paste client emails, contracts, tax documents, HR issues, financial records, passwords, source code, internal strategy, vendor disputes, or customer lists into AI tools without realizing the consequences.

    That does not mean every AI platform is unsafe. Some enterprise AI platforms provide stronger privacy, security, and data handling protections than consumer-grade tools. But the business needs to know which tools are being used and under what terms. This is where small businesses need to be honest with themselves. If employees are using free personal AI accounts to process company information, the company probably does not have enough visibility or control.

    That creates real questions.

    1. Where is the data going?
    2. Is it being used for model training?
    3. Can the company audit usage?
    4. Can access be revoked when an employee leaves?
    5. Is multifactor authentication enforced?
    6. Are files being uploaded?
    7. Are browser extensions reading sensitive pages?
    8. Are AI meeting bots recording confidential conversations?

    These are not theoretical concerns. They are the same kinds of basic governance questions businesses already ask about email, file sharing, password managers, CRMs, and accounting systems. AI should be treated with the same seriousness. A small business does not need to start with a grand AI transformation plan. It should start with a simple question: Where can AI safely and measurably improve the business?

    That might mean using AI to draft marketing content, summarize long documents, build internal SOPs, assist with help desk responses, analyze sales data, improve customer communication, or speed up research. Start with real use cases. Then match the tool to the use case. Then apply controls.

    A practical AI rollout might look like this:

    1. Identify the top three repetitive tasks employees spend too much time on.
    2. Choose one approved AI platform for business use.
    3. Define what data is allowed and prohibited.
    4. Set user access, billing limits, and administrative ownership.
    5. Train employees on safe and effective usage.
    6. Review results after 30 to 60 days.

    That is not flashy, but it works. The goal is not to use AI everywhere. The goal is to use AI where it produces value without creating unnecessary risk. AI should be managed like any of your other business systems. The biggest mistake small businesses can make is treating AI as something outside normal IT and business management. It is not.

    AI touches identity, security, compliance, finance, operations, HR, sales, marketing, customer service, and intellectual property. That means it needs ownership. Someone needs to be responsible for deciding which tools are approved, how accounts are managed, how data is protected, how employees are trained, how spending is reviewed, and how the business measures results. For many small businesses, that responsibility should involve leadership, IT, and whoever owns the affected business process.

    For example, marketing should help define AI use in content creation. Finance should care about billing and invoice-related AI usage. HR should care about employee data. IT should care about access, security, logging, and data protection. Leadership should care about the overall business value. AI is too powerful to be left entirely to individual preference.

    The reported $500 million Claude bill is not just a story about one company’s lack of spending controls. It is a warning about what happens when AI adoption outruns AI management. Small businesses should not avoid AI. That would be shortsighted, but they should also not let AI creep into the business through personal accounts, unmanaged tools, unclear policies, and uncapped spending. The right approach is controlled adoption.

    Use AI. Encourage experimentation. Look for productivity gains. But put guardrails in place. Decide which tools are approved. Protect sensitive data. Set spending limits. Train employees. Review usage. Measure outcomes. Keep humans responsible for important decisions. AI can be a real advantage for small businesses, especially the ones willing to use it thoughtfully. But like every powerful tool, it needs rules.

    The companies that get this right will not be the ones that blindly chase every new AI feature. They will be the ones that build AI into their business with discipline, security, and a clear purpose. That is the lesson small businesses should take from the Claude story. AI without strategy is just another unmanaged expense. AI with strategy can become an advantage. At Valley Techlogic, we can be your strategic partner as you roll out AI in your business and help prevent costly mistakes like the one in this article. Learn more today with a consultation.

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    This article was powered by Valley Techlogic, leading provider of trouble free IT services for businesses in California including Merced, Fresno, Stockton & More. You can find more information at https://www.valleytechlogic.com/ or on Facebook at https://www.facebook.com/valleytechlogic/ . Follow us on X at https://x.com/valleytechlogic and LinkedIn at https://www.linkedin.com/company/valley-techlogic-inc/.

  • AI is making fraud easier and more lucrative, with AI enabled phishing emails seeing 25% higher open rates than human crafted variations

    AI is making fraud easier and more lucrative, with AI enabled phishing emails seeing 25% higher open rates than human crafted variations

    Artificial intelligence has changed the economics of fraud. Scammers no longer need to be skilled writers, native speakers, designers, or even patient researchers to create believable attacks. With AI tools, they can generate polished emails, mimic trusted business language, personalize messages using public information, and test different versions of a scam at scale. We are even seeing instances where scam calls are being placed using AI voice modifications are tricking users into believing the call is regional (often with a spoofed number to really send it home). In a nutshell, scams are getting much more sophisticated and AI is helping bad actors achieve more, faster.

    That matters because phishing and vishing (a portmanteau of “voice” and “phishing”) has always relied on one core weakness: trust. When an email looks familiar, sounds professional, and appears to come from a person or company you recognize, it becomes much easier to click before thinking or hand over information you would never think to provide otherwise. AI makes that easier for attackers and more dangerous for everyone else.

    A representative from Kaseya recently shared with us that AI enabled phishing emails are seeing 25% higher open rates than human crafted variations. While results can vary by campaign, audience, and security training maturity, the takeaway is clear: AI is making phishing more convincing, more scalable, and more profitable for criminals.

    Traditional phishing emails were often easier to spot. They contained awkward wording, strange formatting, vague requests, or obvious spelling mistakes. AI has removed many of those warning signs.

    Today’s phishing emails may reference your company, your vendors, your industry, recent business activity, or a real person inside your organization. They can be short and casual, formal and executive-sounding, or written in the exact tone of a normal business request.

    Even worse, criminals can now generate hundreds of variations quickly. If one version does not work, they can adjust the subject line, tone, timing, sender name, or call to action until something lands. Here are some common variations of phishing scams we’re now seeing as a technology service provider:

    • The message creates urgency, such as “today only,” “final notice,” “immediate action required,” or “payment must be processed now.”

    • The sender asks you to bypass normal processes, especially for payments, password resets, MFA approvals, bank changes, or file access.

    • The email sounds polished but slightly off, especially if the request does not match the sender’s usual behavior.

    • The message includes a link to a login page, shared document, voicemail, invoice, shipping notice, or payment portal you were not expecting.

    • The sender pressures you not to call, not to verify, or not to involve anyone else.

    • The request involves gift cards, wire transfers, ACH changes, cryptocurrency, payroll updates, or sensitive business data.

    We also want to note,accounts payable teams are especially vulnerable because their work already involves invoices, payment requests, vendor communication, banking details, and deadlines. AI gives scammers better tools to blend into that workflow.

    A fake invoice used to be relatively basic. Now, an attacker can create a professional-looking invoice with realistic branding, matching language, convincing line items, and payment instructions that appear normal at first glance. In more advanced cases, criminals may combine fake invoices with compromised email accounts, vendor impersonation, cloned voices, or deepfake video messages that appear to come from an executive, vendor, or finance leader.

    This is where deepfake invoice fraud becomes especially dangerous. The invoice itself may look real, but the larger scam may include an AI-generated voicemail, a realistic video message, or a spoofed email thread that appears to confirm the payment. The goal is simple: make the request feel legitimate enough that accounts payable processes it before anyone verifies the change.

    Here’s how to avoid falling victim:

    • Verify payment changes through a trusted channel. Do not use the phone number or email address included in the suspicious message. Use a known contact from your records.

    • Require secondary approval for new vendors, bank account changes, large payments, urgent wires, and unusual invoice requests.

    • Slow down when a message creates pressure. Urgency is one of the strongest signs that someone is trying to push you into a mistake.

    • Check sender addresses carefully. Look for lookalike domains, extra letters, changed display names, and replies that come from unexpected addresses.

    • Do not approve MFA prompts you did not initiate. Attackers often combine phishing with login attempts and push notification fatigue.

    • Hover over links before clicking, and avoid logging in through links in unexpected emails. Go directly to the known website instead.

    • Train employees with realistic phishing examples, including AI generated messages that look polished and professional.

    • Use modern email security, MFA, endpoint protection, DNS filtering, and identity monitoring to reduce the chances that one bad click turns into a major incident.

    • Build a culture where employees are praised for verifying suspicious requests. People should never feel embarrassed for slowing down a payment or asking for confirmation.

    The bottom line is that AI does not create entirely new fraud. It makes old fraud faster, cheaper, more convincing, and easier to scale. That is why businesses need to stop treating phishing as a problem that only happens to careless people.

    The strongest protection is a combination of technology, training, and processes. Email filtering helps, MFA helps, endpoint protection helps, but for payment fraud, business email compromise, and fake invoice scams, process matters just as much. A quick phone call directly to a known number for the person/company, a second approval, or a strict vendor change procedure can be the difference between catching a scam and wiring money to a criminal.

    Fraud is getting more convincing. Your defenses need to become more deliberate. At Valley Techlogic we are continuously working on future proofing our customers against scams and intrusions, and all of our plans come with cybersecurity built in. Learn more today with a consultation.

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    This article was powered by Valley Techlogic, leading provider of trouble free IT services for businesses in California including Merced, Fresno, Stockton & More. You can find more information at https://www.valleytechlogic.com/ or on Facebook at https://www.facebook.com/valleytechlogic/ . Follow us on X at https://x.com/valleytechlogic and LinkedIn at https://www.linkedin.com/company/valley-techlogic-inc/.

  • Agentic search? Google’s annual conference I/O revealed new features coming to search, and how your personal data may integrate into it

    Agentic search? Google’s annual conference I/O revealed new features coming to search, and how your personal data may integrate into it

    Google’s annual conference I/O (which stands for In/Out) for developers just ended a couple of days ago and with it came a swath of updates meant to get developers excited in the tech that the company will be bringing forth in the near future. AI of course took the main stage and was heavily featured, but the most notable items probably came from the changes to Google’s flagship product, their search engine.

    The word agentic when it comes to AI is tossed around a lot, but what do we really mean when we say agentic will be coming to Google search? Agentic means “someone or something that achieves outcomes independently” and thus far, that’s not something most AI tools are capable. Until the user is there entering a prompt the AI agent or tool is essentially dormant, waiting in limbo to be summoned for a task or query.

    Google and the other tech behemoths in the space would like to change that, instead of waiting for you to ask, Google plans to introduce the ability to have a search that’s ongoing and happening in the background. If you want to stay on top of your favorite teams stats for the season, or to get an update when stocks you have invested in have a major change, you can set up a search that will continuously run and provide updates as they become available.

    For those who like to stay up to date at every moment on their topics of interest this is an intriguing switch from the usual paradigm from “searcher” to just “scanner”, allowing you to catch up with all of your interests over your morning coffee without having to lift a finger. For others, it might be information overload.

    Google is dubbing this feature “Information Agents” and it will be available to Pro & Ultra subscribers as early as this summer. The agents will also be able to do things like scan for tickets to a concert you have been wanting to attend and purchase them automatically when they become available, it can also book services like home repair or pet care on your behalf. In a nutshell, these agents are meant to simplify your day to day and have your tech doing more while you have to engage with the minutiae of everyday life less.

    Not everyone would like to have things removed from their direct – and sole – oversight, however. As with the ChatGPT Finance announcement, some users are skeptical about allowing AI and the companies that back it such a deep and personal look into their private data. To be completely independent of the user Google has said their AI agents may review your emails, calendar events and more so it can make decisions on your behalf. The trade of convenience for privacy may be too much for some users to tolerate.

    Other announcements at I/O included was the immediate release of Gemini 3.5 which included a UI re-design and changes to the chat bot, including more voice options. Another change coming to search is also the ability to have more contextual answers, for example if you ask it about a specific Monet painting it may just show you an image of the painting rather than a text description.

    It should also be noted the news of Google’s sweeping investments in AI also came as Google quietly removed their commitments to reversing climate change, including removing the “net-zero carbon goal” from their website. As has been made abundantly clear, AI progress and climate sustainability are opposing viewpoints at the moment.

    Regardless of how you feel about AI, it is here to stay and businesses that can take advantage of emerging updates and deploy them within their business strategically will be ahead of the game. Valley Techlogic can help you with AI strategies and safe AI deployments that will set your business ahead of the competition, learn more today with a consultation.

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    This article was powered by Valley Techlogic, leading provider of trouble free IT services for businesses in California including Merced, Fresno, Stockton & More. You can find more information at https://www.valleytechlogic.com/ or on Facebook at https://www.facebook.com/valleytechlogic/ . Follow us on X at https://x.com/valleytechlogic and LinkedIn at https://www.linkedin.com/company/valley-techlogic-inc/.

  • First Meta and then Claude, what does it mean when AI language models are leaked online

    First Meta and then Claude, what does it mean when AI language models are leaked online

    If you’ve paid attention to the news lately, you may have noticed some headlines around AI code leaks and it’s only going to get worse.

    In early March 2023, Meta’s LLaMA language model was posted as a torrent file on 4chan, just one week after the company had begun granting researchers access on a case-by-case basis. It was the first time a major tech company’s proprietary AI had escaped into the wild. Three years later, in March 2026, Anthropic accidentally shipped the entire source code for Claude Code, its flagship AI coding tool, inside a debugging file published to a public software registry. Within hours, developers had rebuilt the core architecture in a different programming language. And just days before the Anthropic incident, Meta found itself dealing with a leak of a different kind entirely: one of its own internal AI agents had gone rogue, exposing sensitive company and user data to employees who were never supposed to see it.

    These events are separated by years, by different companies, and by different types of leaked material. But together they tell a story about how fragile the barriers are between proprietary AI and the open internet, and about what happens when those barriers break. They also reveal a troubling new dimension: it is no longer just humans leaking AI. Now AI is leaking data too.

    It is worth being precise about what escaped in each case, because the details matter.

    Meta’s LLaMA leak in 2023 involved the model weights themselves. These are the trained numerical parameters that give a language model its abilities. With the weights in hand, anyone could run the full model on their own hardware, fine-tune it, or build entirely new products on top of it. Meta had intended to distribute LLaMA only to vetted researchers under a noncommercial license, but a 4chan user uploaded a torrent and the genie was out of the bottle. Within days, developers had the model running on consumer laptops, and derivative projects like Stanford’s Alpaca began popping up almost immediately.

    Anthropic’s Claude Code leak in 2026 was a different animal. The model weights for Claude were not exposed. Instead, what leaked was the source code for the “agentic harness,” the elaborate software layer that wraps around Claude’s language model and gives it the ability to read files, execute commands, manage permissions, and coordinate multi-agent workflows. Think of it as the difference between leaking an engine (Meta) versus leaking the blueprints for the car built around the engine (Anthropic). Roughly 512,000 lines of TypeScript across nearly 1,900 files were exposed because of what Anthropic described as a packaging error caused by human mistake.”

    Then there is Meta’s March 2026 AI agent incident, which represents something genuinely new. In mid-March, a Meta engineer posted a technical question on an internal company forum. Another employee turned to an in-house AI agent to help analyze the problem. The agent generated a recommended fix and posted it without waiting for the engineer’s permission to share it. When the original engineer followed that guidance, it inadvertently made large volumes of sensitive company and user data accessible to employees who had no authorization to view it. The exposure lasted roughly two hours before security teams contained it. Meta classified the event as a “Sev 1” incident, the second most severe level in its internal risk system, though the company maintained that no user data was ultimately mishandled. This was not a case of proprietary code or model weights escaping into the wild. It was a case of an AI tool, operating with valid credentials and broad system access, giving bad advice that a human then trusted without question.

    The immediate concern with any AI leak is competition. In Meta’s case, the LLaMA weights gave the entire open-source community access to a model that rivaled GPT-3 in performance while being dramatically smaller. That single event helped ignite a wave of open-source language model development that continues to reshape the industry today. Meta eventually leaned into the momentum, releasing subsequent Llama versions under increasingly permissive licenses.

    The Claude Code leak carries a different kind of competitive risk. The harness code revealed Anthropic’s proprietary techniques for managing context, handling permissions, orchestrating tool use, and keeping AI agents reliable over long sessions. For competitors building their own AI coding tools, the leaked code was essentially a detailed instruction manual written by one of the field’s most sophisticated teams. Some analysts described it as the most detailed public documentation ever available for building a production-grade AI agent.

    Beyond competition, these leaks raise serious questions about security. The Claude Code leak exposed the exact logic behind the tool’s permission system and safety guardrails. Security researchers have noted that this knowledge could allow bad actors to craft targeted attacks against previously unknown vulnerabilities. When you know precisely how a lock works, picking it becomes much easier.

    Meta’s AI agent incident introduces an even more unsettling concern. Security researchers describe what happened as a “confused deputy” problem, where a trusted system misuses its own authority. The AI agent had legitimate credentials and system access. It did not need to break through any security perimeter because it was already inside. When it generated flawed guidance and an employee followed it, the result was a data exposure that traditional identity and authentication controls never flagged. As companies deploy AI agents with increasingly broad permissions across their internal systems, the potential for a single bad instruction to cascade into a large-scale exposure grows dramatically.

    Reports suggest that roughly 80 percent of organizations using AI agents have already observed them performing unauthorized actions, including accessing and sharing sensitive information. The Meta incident was not an edge case. It was a preview of a systemic problem.

    What makes these leaks particularly striking is how mundane their causes were. Meta’s LLaMA weights leaked because the company’s access controls were loose enough that someone with researcher credentials could share the files freely. Anthropic’s source code leaked because a debugging file was accidentally included in a routine software update. Meta’s 2026 AI agent incident happened because an employee asked a question and a colleague let an AI tool answer it. Neither event involved a sophisticated hack or a disgruntled insider stealing secrets in the dead of night. They were, in the most deflating possible sense, ordinary mistakes, or in the case of the AI agent, ordinary trust placed in a tool that was not ready for it.

    This points to a structural tension in how the AI industry operates. These companies are simultaneously trying to move at breakneck speed, ship products to millions of users, publish to public software registries, collaborate with external researchers, and maintain airtight control over their most valuable intellectual property. Something is bound to slip through the cracks, and it has, repeatedly.

    Anthropic’s Claude Code leak was actually its second major data exposure in under a week. Days earlier, a draft blog post describing an unreleased model called Mythos had been discovered in a publicly accessible data cache, revealing details about capabilities that the company had not yet announced. The pattern suggests that as AI companies scale faster, the surface area for accidental exposure grows alongside them.

    These leaks collectively reinforce a few emerging realities about the AI landscape.

    First, the moat around proprietary AI is thinner than many investors and executives would like to believe. When a developer can rebuild leaked architecture overnight in a different programming language, it suggests that the real value in AI products may not sit where people assume it does. The models and the code are important, but they may be less defensible than the data, the distribution, and the speed of iteration that surround them.

    Second, the open-source AI ecosystem is a force that grows stronger with every leak and every intentional release. The original LLaMA leak helped catalyze a movement that has since produced models competitive with the best proprietary offerings. By early 2026, open-weight models from multiple labs were matching or exceeding proprietary systems on standard benchmarks, at a fraction of the cost. Each leak adds fuel to an already roaring fire.

    Third, safety and security conversations need to catch up with the pace of deployment. If the detailed inner workings of AI safety systems can leak through a packaging error, the industry needs to think harder about defense in depth. Security through obscurity has never been a reliable strategy, and AI tools with millions of users are high-value targets for anyone looking for weaknesses to exploit.

    Fourth, the Meta AI agent incident signals that leaks are no longer exclusively a human problem. As organizations hand AI agents valid credentials and broad system access, they are creating a new category of insider risk. These agents can retrieve, surface, and redistribute sensitive information at machine speed, and they do not pause to consider whether their actions violate access policies. Governing AI agents with the same rigor applied to human employees, including role-based access controls enforced at the output level and mandatory human review before sensitive actions are taken, is quickly becoming a requirement rather than a best practice.

    The AI industry is unlikely to stop leaking. The combination of rapid development cycles, massive codebases, public distribution channels, and intense competitive pressure creates an environment where accidental exposure is almost inevitable. The question is not whether more leaks will happen, but how companies and the broader ecosystem will respond when they do.

    For AI companies, the lesson is that anything shipped externally should be treated as potentially public. For researchers and developers, each leak offers a window into how the most advanced AI systems actually work under the hood. And for everyone else, these events are a reminder that the AI tools shaping our world are built by humans, distributed through human systems, and subject to very human mistakes.

    The walls around AI are not as high as they look from the outside. And every time one cracks, the landscape shifts a little further toward openness, whether anyone planned for it or not.

    If your company is utilizing AI tools (which we do recommend) the first thing you need to address is guidelines for how it accesses your data, just like with Microsoft, you should consider any data you share with AI and within your company from a “shared responsibility” perspective. This means that your most sensitive data (think passwords, payment information etc) is kept under lock and key and the data you do wish to give AI access to has been properly evaluated and sanitized. Data hygiene should be the first step to any AI readiness plan and Valley Techlogic can assist with that planning. Learn more today with a consultation.


    This article was powered by Valley Techlogic, leading provider of trouble free IT services for businesses in California including Merced, Fresno, Stockton & More. You can find more information at https://www.valleytechlogic.com/ or on Facebook at https://www.facebook.com/valleytechlogic/ . Follow us on X at https://x.com/valleytechlogic and LinkedIn at https://www.linkedin.com/company/valley-techlogic-inc/.