Agentic security in the enterpriseMiddleLesson 1576 min read

Governing agents: approvals, audits and accountability

Security controls need a process around them: who approves an agent, how risky it is, who owns it, and how you check it is still safe months later.

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In 60 seconds

Governing agents: approvals, audits and accountability

Security controls need a process around them: who approves an agent, how risky it is, who owns it, and how you check it is still safe months later.

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In simple words
Before a new bus goes on the road, someone checks it, gives it a number plate, and names who is responsible for it. Every year it is checked again. AI agents need the same: a check before they start, a name tag, and regular check-ups.
Governance is the set of decisions and routines that keep all the controls in this track working over time. Without it, the first agent is carefully reviewed and the fiftieth is not. Good governance is light for low-risk agents and strict for high-risk ones.

Risk tiers

TierExampleWhat it needs before going live
LowDrafts internal text from public information; no tools that change anythingRegistration in the inventory, owner, standard settings
MediumReads internal data for staff; writes to low-risk systems; reversible actionsSecurity review, scoped identity, logging, gateway policy
HighTouches money, personal data, production systems, or reads untrusted content and can actThreat model, red-team test, human approval steps, kill switch tested, sign-off by risk owner
A simple way to set the tier: look at the data it can reach, the actions it can take, whether those actions can be undone, whether it reads untrusted content, and who is affected if it goes wrong.

The go-live approval

  1. 1

    Intake

    The business owner fills a short form: purpose, data, tools, users, vendor, expected volume.
  2. 2

    Tiering

    Security assigns a tier using fixed questions, so similar agents get similar treatment.
  3. 3

    Review

    Checks match the tier: identity and scopes, tool list, gateway rules, logging, approvals, testing.
  4. 4

    Sign-off

    A named business owner accepts the remaining risk; security confirms controls are in place.
  5. 5

    Register

    Inventory entry with owner, tier, review date, and links to the design and test results.

After go-live

  • Periodic access reviews: the owner confirms the agent still needs each permission and tool. Unused ones are removed.
  • Change control: new tools, new data sources, a new model or major prompt changes trigger a fresh review.
  • Audit trails: tool-call logs, approvals and configuration changes are kept for an agreed period and can be searched.
  • Metrics: incidents, blocked calls, approval rates, overrides. A sudden change is worth a look.
  • Retirement: when an agent is no longer needed, its identity, grants, memory and data are removed on purpose.

Mapping to frameworks

You do not need to invent your own structure. Well-known frameworks give you a shared language with auditors and regulators:
FrameworkWhat it isHow agent governance fits
NIST AI RMF 1.0A voluntary US framework with four functions: Govern, Map, Measure, ManageGovern: policy, roles, approvals. Map: inventory and context. Measure: testing and metrics. Manage: controls, incidents, retirement
NIST AI 600-1The Generative AI Profile of the AI RMFLists risks specific to generative AI that agents inherit
ISO/IEC 42001An international standard for an AI management system; organisations can be certified against itYour approval process, risk tiers, reviews and records become part of the management system
OWASP Top 10 for LLM ApplicationsA list of common LLM application risksLLM06 Excessive Agency and LLM01 Prompt Injection map directly to agent controls
Note
Laws are changing too. The EU AI Act entered into force in 2024, with obligations phasing in over several years. In India, the Digital Personal Data Protection Act 2023 applies whenever agents handle personal data. Ask your legal team which rules apply to your agents; this lesson is not legal advice.

Accountability

Blurry accountability
  • "The AI decided"
  • Owner listed as "IT"
  • Approvals recorded as "system"
  • Nobody reviews old agents
Clear accountability
  • A named business owner for every agent
  • A named approver for every high-risk action
  • Records showing who approved the agent and what they saw
  • Review dates that trigger reminders and, if missed, suspension
Try this. Pick one agent in your company or an imaginary one. Using the tier table, which tier is it? What is the one control it is missing for that tier?

Watch and read more

Lab

An agent intake form, a tiering rule and a completed approval record for one agent.

~35 min

The problem

Design the governance paperwork for your company's agents. Keep it short enough that people will actually use it.

  1. Write an intake form of no more than 10 questions.
  2. Write a tiering rule: which answers make an agent low, medium or high.
  3. Fill in the form for one agent (real or imaginary) and assign its tier.
  4. Write its approval record: controls required for that tier, which are done, who signed off, next review date.
  5. Map your process to the four NIST AI RMF functions in one line each.
Starter codetext
AGENT INTAKE (draft)
1. Agent name and purpose (one sentence):
2. Business owner (named person):
3. Users (who talks to it or benefits from it):
4. Data it can read (and its classification):
5. Actions it can take; which cannot be undone:
6. Does it read content from outside the company (email, web, tickets, partner agents)?
7. Vendor, model provider and where data is processed:
8. Expected volume (tasks per day):
9. Approvals or human checks in the design:
10. How is it switched off?

You are done when

Hard questions

Try to answer before you reveal. If you can answer these, you understood the lesson.

Q1Two teams answer the intake form differently for very similar agents, and get different tiers. How do you fix that?Reveal

Make the questions more factual and less open to opinion (for example "Can it send email outside the company? yes/no" instead of "Is it risky?"). Have security confirm answers against the actual configuration, and keep examples of tiered agents so reviewers can compare. Consistent tiering is a sign the process is fair and trusted.

Q2An auditor asks, "How do you know the agents in production are the ones you approved?" What evidence would you show?Reveal

The inventory with approval records, matched against what is actually running: identities in the identity system, OAuth grants, gateway policies, and tool-call logs showing only registered agents. Periodic discovery (as in the shadow AI lesson) shows you would notice unapproved agents. A gap between the inventory and reality is itself a finding to fix.

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Questions people ask

Will this process slow down innovation?

Not if it is tiered. Low-risk agents should be approved in days with a short form. Strict review is kept for agents that touch money, personal data or production. A clear, fast process also reduces shadow AI, because people are more willing to ask.

Who should own AI governance?

Usually a cross-functional group: security, data protection, legal, risk and business leaders. Security runs the technical review; business owners accept risk for their agents; leadership sets the risk appetite.

Do we need ISO/IEC 42001 certification?

Not necessarily. Some organisations seek it because customers or regulators value it. Others use it or the NIST AI RMF as a guide without certifying. Either way, the frameworks help you organise what you already do.

What happens if an owner misses a review?

Set a clear rule in advance, for example a reminder, then escalation, then suspension of the agent's access. A review date nobody enforces is not a control.

Lesson test

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