# The AI Agent Problem Nobody Wants to Admit: Jake Williams Has a Framework for It
When OpenAI's agents started probing Hugging Face infrastructure, most of the industry's response was a collective shrug dressed up as analysis. The real question — the one that keeps enterprise security architects awake — wasn't about OpenAI specifically. It was simpler and more uncomfortable: *what happens when your AI agents do that?*
Jake Williams, one of the sharper enterprise security minds in the field, decided that was the right moment to stop waiting for someone else to answer it. His CUSTODY framework, released this week and explained at the Dark Reading News Desk, is an attempt to give organizations something concrete — a set of constraints that governs what agentic AI systems can actually do inside a network before they cause damage that no one authorized.
## The Agentic Problem Is Different From the LLM Problem
For the last two years, the enterprise security conversation around AI has been dominated by the wrong threat model. Prompt injection, data leakage through chat interfaces, training data poisoning — real issues, all of them, but fundamentally bounded. A chatbot that leaks sensitive documents is bad. An autonomous agent with network access that can be manipulated into lateral movement is categorically worse.
The difference is agency. LLMs respond to prompts. Agents *act*. They hold credentials, execute code, call APIs, browse internal systems, trigger workflows. The attack surface is not just the model — it's everything the model can reach.
Williams' concern, articulated clearly in his Dark Reading appearance, is that enterprises are deploying these systems faster than anyone has figured out how to constrain them. CUSTODY is his attempt to name the problem well enough that organizations can start reasoning about it systematically.
## What CUSTODY Actually Constrains
The framework centers on a principle that sounds obvious until you watch it get violated in practice: AI agents should operate with the minimum necessary access, in a monitored environment, with clear boundaries on what actions they can take autonomously versus what requires human confirmation.
That's not novel as a concept — it's essentially least-privilege applied to agentic systems. What Williams brings to it is specificity about where enterprises consistently fail:
Credential handling is the first place things go wrong. AI agents need credentials to do their jobs, and most current deployments hand those credentials over with no expiration, no scope limitation, and no audit trail that connects specific agent actions to specific access events. An agent with a long-lived, broad-scoped API key is a target. When that agent gets compromised or manipulated, the attacker inherits everything the key could reach.
Action scope is the second failure point. Enterprises rarely define, in advance, what an agent is and isn't allowed to do. The result is that agents operate with implicit permissions — they can do whatever their underlying credentials allow, which is almost always more than anyone intended.
Auditability rounds out the core problem. When something goes wrong with a human employee, you have logs, access records, a timeline. When an AI agent does something unexpected, many organizations have almost nothing — a vague record that the agent ran, but no granular trace of the reasoning chain, the data it accessed, or the specific actions it took. Forensics becomes guesswork.
## Why Hugging Face Made This Urgent
The OpenAI-Hugging Face incident was a demonstration, not an anomaly. The specific mechanics — AI systems being used to probe, test, or interact with external infrastructure in ways that looked like reconnaissance or abuse — matter less than what it revealed about the broader threat landscape.
AI agents calling external services is now baseline behavior. An agent that scrapes documentation, calls APIs, accesses code repositories, or interacts with third-party platforms is not doing anything unusual. Which means any organization running agentic workflows has implicitly accepted the risk that those agents could be manipulated into doing something harmful against external parties — or, more relevantly for defenders, that external actors could manipulate an agent into doing something harmful internally.
Williams read that incident as a forcing function. The attack pattern is established. The question isn't whether AI agents will be used as vectors — it's whether the organizations deploying them have any framework for limiting the damage when that happens.
## HackWire Analysis
The CUSTODY framework arrives at a peculiar moment: late enough that enterprises are already running agentic systems in production, early enough that most of them have no coherent security model for those systems. That gap is exactly where real damage happens.
What Williams is doing — and what the security community has been frustratingly slow to do — is applying decades of network security thinking to a new class of software. The principle of least privilege didn't emerge from theoretical research. It came from watching what happened when users and processes had access they didn't need. We're at that same empirical moment with AI agents, except the timeline is compressed and the blast radius is larger because these systems are being deployed at scale before anyone has burned their fingers badly enough to slow down.
The Hugging Face incident matters here not because of its specific details but because of its timing. It made the threat model legible to decision-makers who weren't previously paying attention. Security practitioners have understood the risk for months. Getting organizational leadership to prioritize mitigation requires a visible incident, and this one was visible enough.
The missing piece in most CUSTODY-adjacent conversations is operational friction. Enterprises resist security controls that slow agents down, because the entire value proposition of agentic AI is autonomous action at speed. Any framework that requires human confirmation for every significant action defeats the purpose. Williams' answer — and it's the right answer — is to define the boundaries precisely enough that agents can operate autonomously within a safe envelope, while flagging anything outside that envelope for review. The hard work is defining the envelope before deployment, not after something breaks.
What the broader industry should take from this: the organizations that build mature AI agent security practices now will have a significant operational advantage when — not if — agentic AI becomes a primary attack vector target. Building the framework when you're not under pressure is dramatically easier than building it during an incident.
— HackWire Editorial
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