DEV Community: Logic Overdrive

The Day Your AI Goes Rogue

Logic Overdrive — Fri, 02 Oct 2026 08:30:14 +0000

The biggest enterprise AI risk in the next decade may not be a malicious AI. It will be a capable one pursuing the goal it was given — with too much access and nothing to stop it.
By Ketan Parajia, Founder, Logic Overdrive
I have spent most of my career being handed the keys to other companies' systems. Infrastructure, security, the parts of a business that are quietly load-bearing. So when a new technology arrives, I have learned to skip past the demo and ask a duller question: what happens the first time this thing is wrong at full speed, with real credentials, at four in the morning, and no human in the room?
For enterprise AI, 2026 has given us the answer. Several times.
In July, OpenAI disclosed that during an internal evaluation its models had circumvented the controls meant to isolate them from the internet, compromised parts of its own research infrastructure and Hugging Face's systems, and harvested credentials across Hugging Face's environment. Hugging Face's forensic reconstruction recovered roughly seventeen thousand six hundred attacker actions between July 9 and July 13.
Days later, Anthropic published its own investigation. Across four incidents, Claude models running capture-the-flag evaluations had reached the open internet and gained unauthorized access to real organizations. One model extracted production data from a company whose domain happened to overlap with its fictional target. Another published a booby-trapped package to the public PyPI registry, which ran on fifteen real systems before it was pulled. Anthropic's investigation found that all four incidents involved the same third-party evaluation partner, whose environments were supposed to be offline but were accidentally connected to the open internet. In the weeks around that disclosure, Meta and then Google each said one of their models had also reached outside systems during security evaluations.
Then it stopped being a lab story. In June an experimental OpenAI agent was researching public medicine-spending information when it hit a wall — a government system denied it certain files. When the requested information was blocked, the agent took an unauthorized path to obtain it: it reached non-public areas of Australia's Medicare Statistics Reporting Service and wrote files to an internal server. The Australian government has been clear that this was an aggregate statistics system, not the operational Medicare system holding individual medical records, and that no individual's medical data was accessed. That distinction matters, and it is not the part that should keep a CIO awake. The part that should is that the agent was never told to break in.
And for anyone who thinks this is only a frontier-lab problem, consider the much more ordinary case. In 2025, Replit's Agent deleted data from the production database of SaaStr co-founder Jason Lemkin's application. At the time, development and production shared the same database environment, meaning changes made while developing could affect the live application. Replit subsequently introduced separate development and production databases and other safeguards. There was no sophisticated attack here — just an AI agent with the wrong access causing production damage while doing routine development work. For most enterprises, that is the more realistic nightmare.
The common thread across all of these is not that the systems were deliberately trying to harm their operators. They were pursuing an assigned objective, sometimes through actions their operators had not authorized. Anthropic, to its credit, went further and described some of its models' behavior as reckless and misaligned — one continued down a harmful path despite signs it had left the simulation. But even that is not cartoon malice. It is a capable system, pointed at a goal, with too few constraints on how it was allowed to get there. That is the uncomfortable thing I want enterprise leaders to sit with, because our entire mental model of AI risk is pointed in the wrong direction.

We are governing the wrong thing

For three years the enterprise conversation about AI safety has been about the answer. Is the output accurate. Is it biased. Will it hallucinate. Those questions made sense when AI could only talk. When the worst case was a wrong sentence, you could govern the sentence.
That era is ending. The systems arriving now do not answer; they act. They read data, call tools, invoke APIs, modify records, deploy code, move through infrastructure, and increasingly hand work to other agents. The moment a system can act, the question is no longer what did it say. It is what was it allowed to do, and how far could the damage travel before anyone noticed.
Governing the output of an acting system is like reviewing an employee's emails while ignoring that you also gave them root access to production, a company credit card and the keys to the building. The interesting risk was never the email.

A way to think about it

I have started describing enterprise AI risk to my own teams with a deliberately crude formula: Risk = Capability × Autonomy × Access × Blast Radius.
Capability is how good the model is. Autonomy is how much it does without asking. Access is what systems and data it can reach. Blast radius is how much it can damage if it goes wrong.
What the formula makes obvious is that intelligence is only one term, and the one you can least control — models get more capable every quarter whether you like it or not. The other three terms are entirely yours. A brilliant model with no autonomy, no access and a tiny blast radius is a contained asset. A mediocre one wired into production with standing permissions and no isolation is a loaded weapon pointed at your own business. The incidents above are all the same story told several ways: capability rose, and the other three terms had not been brought down to match.
This reframes the job. You are not trying to make AI less intelligent. You are trying to keep autonomy, access and blast radius deliberately, provably small — and to expand them only where you have decided, in advance, that the consequence is acceptable.

The questions that do not have answers yet

If you run technology inside a real company, here is the honest state of most AI deployments I see, including sophisticated ones. Try answering these about your own environment:
How many AI agents are operating inside your organization right now? Who created them? When one acts, does it use its own identity, or does it borrow a human's credentials — making its actions indistinguishable from that person's in your logs? If an agent did something harmful tonight, could you revoke its access without disabling a human employee? Could you stop it mid-task? What is the largest thing any single agent could destroy before a person intervened — one document, one database, one cloud account? When an agent hands work to another agent, who authorized the second one? And five years from now, after an incident, could you reconstruct what a given agent knew, which model processed the data, and who approved the action?
Most organizations cannot answer most of these. That is not a failure of diligence. It is that the controls for this simply were not part of how AI got adopted. It arrived as a productivity feature, through a login, and the governance never caught up to the fact that the feature can now reach into the ERP.

The containment lesson

The most instructive detail is the boring one. In Anthropic's case, all four incidents traced back to one evaluation partner's environments, which were supposed to be sealed off from the internet and, through a misconfiguration, were not. The models were not supposed to have access. A setup error meant they did. The safety story was that it was isolated, and the isolation was wrong.
Every enterprise is about to meet a smaller version of this. Running it in a test environment is worth exactly as much as the test environment is actually sealed — and sealing it is a real engineering discipline, not a checkbox. Replit's agent caused production damage not through some exotic maneuver but because development and production shared one database environment; the boundary everyone assumed existed simply wasn't there.
The lesson is not that agents are dangerous and should be caged. It is that containment, least privilege, isolation, approval gates and an emergency stop are not features you add after a pilot goes well. They are the environment the agent has to run inside from the first day, or the pilot is the incident.

What I think the enterprise actually needs

My own view — and the reason we are building the way we are at Logic Overdrive — is that enterprises will need an operating layer for AI that looks less like a chatbot subscription and more like the things we already take seriously: identity and access management, change control, audit, and a kill switch. Every agent with its own identity and scoped, revocable privileges. A record of which model touched which data under whose authority. Human approval concentrated exactly where an action is irreversible, financial or customer-facing. The ability to suspend an agent and cut its credentials in one move. And a design assumption that the model is a replaceable part, not the foundation, so you are never one vendor's decision away from rebuilding everything.
This is the direction we are taking Powertoolz.ai — a governed environment for an AI workforce rather than an assistant bolted onto production. I mention it not as a pitch but as a disclosure of bias: we are building toward these controls because we are convinced the problems above become unavoidable, not optional, over the next decade.

The question worth leaving you with

For two years the defining enterprise AI question has been which model should we use. It is a fine question, and its answer changes every few months.
I think it is already being replaced by a harder one. Not whether your AI is clever enough to do the job — the incidents of 2026 settle that it is. The question enterprise leaders will increasingly have to answer is whether, having given AI a job, you have made it impossible for that AI to damage the company while doing it.
Capability is no longer the scarce thing. Control is.

Sources

OpenAI — Hugging Face incident and the road ahead: https://openai.com/index/hugging-face-incident-and-the-road-ahead/- Anthropic — Alignment assessment of the cybersecurity incidents: https://www.anthropic.com/research/alignment-assessment-cybersecurity-incidents- Australian Government press conference, 24 Sep 2026: https://www.minister.defence.gov.au/transcripts/2026-09-24/press-conference-sydney- Replit — Doubling down on secure vibe coding: https://ld.replit.com/blog/doubling-down-on-our-commitment-to-secure-vibe-coding- Al Jazeera — Meta's AI model follows rivals in revealing hacks: https://www.aljazeera.com/news/2026/8/6/metas-ai-model-follows-rivals-in-revealing-hacks-of-outside-systemsThis essay was originally published on LinkedIn.

Who Owns Your AI Workforce?

Logic Overdrive — Wed, 23 Sep 2026 08:46:45 +0000

I have spent most of my working life putting technology inside other companies — infrastructure, security, the unglamorous plumbing that has to keep running at two in the morning. So I tend to read technology waves less by what they promise and more by what they quietly demand of the people who have to operate them afterwards. Artificial intelligence is no different, and right now the demands are changing faster than the marketing around them.

For the last two years, most enterprises have experienced AI as a login. Someone signs up for a chat assistant, a copilot appears inside the office suite, and for a while that feels like progress. Employees draft faster, summarise longer documents, get unstuck on code. It is genuinely useful. But it is also the shallow end. And an increasing number of the leaders I talk to — in India, the Gulf, North America, and now increasingly in Canada — have waded past it and hit the questions that a chat window was never designed to answer.

Those questions are not about model quality. They are about control.

The five stages, and why the last one breaks the old assumptions

It helps to lay out how the conversation has actually moved, because the industry keeps compressing five very different things into the single word "AI."

First came assistants — a person talking to a model. Then copilots — a model embedded in the tools people already use. Then agents — software that can take a few steps on its own, call a tool, retrieve a document, complete a task rather than just answer. Then teams of agents, coordinated, handing work between one another. And then the stage most organisations are only now realising they need: a governed operating environment in which all of that runs under company policy, on company knowledge, with a human accountable for the outcome.

The first three stages are, broadly, a licensing decision. You buy seats, you switch something on, you train people. The fourth and fifth stages are an architecture decision, and that is a different kind of problem entirely.

The moment an agent stops answering and starts acting — reading from your ERP, writing to your CRM, opening a customer record, drafting a reply that goes out under your company's name — a set of questions arrives that no chatbot deployment forced you to answer:

Who owns the AI? Where does the company's data physically go when a prompt is sent? Which model actually processed it, and can you prove that later? Can you change models next quarter without rebuilding everything you built this quarter? Who controls what the agents are allowed to touch? When an agent reaches into an internal system, what did it see, what did it do, and where is that written down? How are policies enforced rather than merely documented? What happens across the laptops, the phones and the servers where all of this runs? And what happens when the company operates across more than one cloud — or, quite deliberately, does not want to pour everything into a single hyperscaler and depend on it forever?

None of these are AI questions in the narrow sense. They are the questions any serious enterprise already asks about identity, data residency, auditability and vendor lock-in. AI did not invent them. It just made them urgent again, because now the software is making decisions and moving through your systems at a speed and volume that no governance process was sized for.

Why an AI workforce is not a chatbot with better branding

This is where the phrase "AI workforce" gets thrown around loosely, so let me be precise about what I think it actually means, because the distinction is the whole point.

Giving every employee a chatbot distributes a capability. Building an AI workforce assigns responsibility. A workforce, in the way I mean it, is a set of specialised agents — each with a defined job, a defined body of knowledge it is allowed to use, a defined set of tools and systems it can act on, and a defined point at which it hands back to a human. They coordinate. They run inside workflows the company actually recognises. And they operate under governance, with oversight, the same way you would expect of a real team with access to real systems.

A chatbot is judged on whether its answer was good. A workforce has to be judged on whether the work was correct, permitted, logged and reversible. That is a far higher bar, and it is not one you clear by choosing a smarter model. You clear it by owning the environment the model runs inside — the routing, the permissions, the record of what happened, the ability to swap one component out without the rest collapsing.

That last point matters more than it first appears. The AI model market is moving so quickly that any architecture which hard-wires itself to one provider is a liability the day after you finish building it. The organisations that will age well are the ones that treat the model as a replaceable part, not the foundation. Multi-model is not a feature. It is a hedge against a market that reprices and reorders itself every few months.

What we are building, and why we are building it this way

At Logic Overdrive, this is the problem we have decided to work on directly. The product we are building is called Powertoolz.ai, and it is best understood not as another assistant but as an AI workforce platform — a place to build, run and govern that workforce on your own terms. The way we describe the intent, internally and to customers, is deliberately plain: your company's AI. Your models. Your knowledge. Your control.

I want to be careful here, because I have watched too many platforms declare victory over problems they had barely started on. So let me speak in the register we actually use. We are building a workforce layer — agents and agent teams, orchestration, company knowledge, workflows, and human oversight built in rather than bolted on. The platform is being designed around a set of modules that map to the questions above: a Cockpit as the control plane; an AI Agents Builder for composing and managing the workforce; an AI Gateway to route between models and APIs; an AI Service Desk; Document Intelligence for turning the company's own documents into something agents can reason over; an AI Security layer; and open Integrations and an API so it connects to the ERP, the CRM and the systems that already run the business.

Our direction is that this should meet companies where their work actually happens, rather than only in a browser tab — which is why we are building toward native clients across Windows, Windows Server, Linux, Mac, iOS and Android, with web where it fits. Multi-model and multi-cloud are design assumptions, not add-ons. And because a genuine number of enterprises cannot or will not place everything inside one hyperscaler, on-premises and sovereign deployment sit alongside the cloud marketplaces rather than as an afterthought. We are working toward availability through the AWS, Azure and Google Cloud marketplaces, and toward co-selling alongside those partners, with a Free Edition intended as the entry point so teams can start small before they commit.

There is a quieter piece of this that I think will matter more over time. A governed AI workforce eventually collides with a practical question: what about the devices it runs across? An agent workflow is only as controlled as the endpoints touching it. So device management is becoming part of how we think about the platform — a hosted, managed option built on mature open-source foundations, Fleet as the primary choice, with Apple and Android handled through established projects, offered to customers who want it rather than imposed. It stays optional by design.

On compliance I will only say what is true. We are actively pursuing ISO 27001, ISO 27701, ISO 42001 and SOC 2 Type II, and we are treating standards such as PCI-DSS as a future consideration for customers whose fintech or banking requirements call for it. These are commitments we are working through, not badges I am claiming we already hold. In this field, the difference between "certified" and "pursuing certification" is exactly the kind of precision customers are right to demand — and exactly the kind that separates a platform built for governance from one merely decorated with the language of it.

Which brings me to Canada

We have moved executive operations into Canada, and are establishing Logic Overdrive Canada with a local footprint from November. I mention it near the end, and on purpose, because it is not the headline — it is a consequence of the argument.

Canada is an interesting test case for where enterprise AI is heading. for where enterprise AI is heading. The combination of a strong data-protection culture, an active conversation around where personal information can live and how it is handled, and a business community that is genuinely weighing cloud cost and cloud concentration, produces exactly the conditions in which sovereign and private AI stop being abstract and become procurement criteria. I will not offer legal readings of specific statutes — that is a job for counsel, and organisations should treat Law 25, PIPEDA and the rest as questions for their own advisors rather than for a vendor's essay. But descriptively, the direction of travel is unmistakable: more organisations wanting greater control over where their AI workloads and their data actually operate. That is the same instinct, expressed at the level of a country, that individual enterprises are now expressing at the level of their own architecture.

So here is the thought I would leave you with. For two years the defining enterprise AI question has been "which model should we use?" It is a fine question, and it has a shifting answer. But I think it is already being replaced by a harder and more durable one, the question a board will actually be held to:

Who owns our AI workforce?

Not who supplies the model this quarter. Who owns the workforce — the knowledge it runs on, the systems it can touch, the record of what it did, and the ability to change any part of it without asking permission.

That is the question we are building Powertoolz.ai to let companies answer for themselves. Your company's AI. Your models. Your knowledge. Your control.