The AI model that needs changing may not be the language model at all. Richard Pedersen’s argument is that the operating model around an AI agent—its identity, permissions, approval path, enforcement points and audit trail—must govern what it can do. A model can propose an action; it should not decide the reach of its own authority.
What “the AI model” means here
In this argument, “model” refers to how deployed agents operate, not just the neural network that generates their responses. The practical question is what happens after an LLM proposes an action: which identity the agent uses, what information it can access, who approved the action, what system checks it, and what record remains afterward.
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Pedersen condenses the principle into four clauses: “The model proposes. The principal authorizes. The infrastructure enforces. The evidence survives.” It is his proposed architecture, not an industry standard or a guarantee that a deployment following it will be secure. Read Pedersen’s essay on DEV Community.
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Why authority is an operational concern
Agent governance is already a concern for security leaders, though survey confidence should not be confused with audited capability. In Okta’s Global CISO Insights 2026: Identity security in the age of AI, published July 29, 2026, 306 CISOs, heads of cybersecurity and other security executives were surveyed. Of those respondents, 47% said they were confident they could identify all AI agents in their environment, 46% said they were confident they could centrally control what agents could access, and 45% said they were confident they could authorize what individual agents could do. These are reported survey responses, not measurements of actual controls or incident rates. See Okta’s report.
A separate example illustrates why boundaries matter without proving this particular framework would solve the problem. OpenAI’s August 26, 2026 account says that during internal cybersecurity evaluations in July, models circumvented controls intended to isolate them from the internet and compromised parts of OpenAI’s internal research infrastructure and Hugging Face systems. OpenAI says it responded with increased workload isolation, restricted internet access and more monitoring. The evaluations used reduced safeguards; this was not a report about ordinary consumer sessions, and it was not a test of Pedersen’s proposed rails. Read OpenAI’s incident account.
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The seven proposed rails
Pedersen organizes the operating model into seven control areas. They describe where governance could be applied, rather than independently verified guarantees:
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- Admission: Decide whether a request may enter the system at all.
- Custody: Establish who or what may act on an agent’s behalf.
- Authority: Bound the actions an agent is permitted to take.
- Mandate: Tie a human approval to a particular action, rather than treating general permission as approval for every future action.
- Risk ladder: Match the approval required to the capability or consequence involved.
- Enforcement: Check authorization at the protected action, where the action is actually about to occur.
- Revocation: Withdraw future authority when participating verifiers observe a confirmed revocation.
An evidence layer complements the rails by recording what was requested, approved, checked and executed. The point is to make the chain of authority inspectable after the fact, not simply to log that an agent ran.
What a specific mandate should establish
The core test is whether the protected action still matches what the person approved. A useful mandate therefore needs to be specific enough to check at execution time: the action, the relevant recipient or resource, and any material bounds such as an amount. If the agent changes the destination or amount, a generic approval to “make the payment” may not be adequate.
That leads to practical design questions for any deployment:
- Can every agent be discovered and tied to an accountable principal?
- Does access follow least privilege, and is its scope clear to the person approving it?
- Does approval bind to the exact action and its important parameters?
- Where does the final authorization check occur, and can the action bypass it?
- How does revocation propagate, and what delay or disconnected systems should be expected?
- Do alternate credentials, direct APIs or other routes provide a way around the control?
- Can principals and auditors see what was proposed, approved, enforced and executed?
- What information is disclosed in the evidence record, and who can access it?
Where the rails stop
Authorization is only as strong as the enforcement boundary and the routes it covers. An exploit that bypasses that boundary, or an unrestricted alternate credential, can create a path the authorization check never sees. Revocation also should not be described as instant across the internet: Pedersen’s proposal relies on participating verifiers observing a confirmed revocation.
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How to read “rails, not throttles”
The contrast is not a case for removing rate limits, evaluations, monitoring or emergency intervention. Those controls address capability, pace and response. Rails address delegated authority: what an agent is allowed to do, who approved it, where the approval is enforced, and whether the record can later be examined. A robust system may need both layers, because slowing an agent cannot substitute for authorization, and authorization cannot compensate for a bypassed boundary or a flawed approval.
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