Runtime Policy Enforcement for AI Agents

Most AI governance programs can write policies.

Very few can answer this question:

"Which exact rules were enforced at runtime when the agent took that action?"

Without that answer, governance stays theoretical.

The missing primitive is a deployable governance artifact—something that can be:

Reviewed and approved
Versioned and promoted
Cryptographically signed
Deployed consistently to enforcement points
Proven in audit

That artifact is a Policy Bundle.

Why "Policies" Don't Work Unless They're Deployable

In traditional security, mature controls have a lifecycle:

Authored
Approved
Implemented
Monitored
Tested
Evidenced

In many AI governance stacks, "policy" is:

A PDF
A wiki page
A set of prompt guidelines

That may influence behavior, but it doesn't constitute a control environment.

A real control environment requires:

Deterministic enforcement where possible
Provable provenance for what ran in production

What Is a Policy Bundle?

A Policy Bundle is a versioned package of machine-executable controls that runtime enforcement points (gateway/sidecar) can evaluate in real time.

It includes:

Component	Purpose
Control definitions	Rules + actions
Risk tier logic	Enforcement intensity per tier
Tool constraints	From Tool Registry
Escalation/approval requirements	Human-in-the-loop triggers
Degraded-mode behavior	Fallback when uncertain
Evidence requirements	What must be logged
Cryptographic signatures	Tamper evidence

Think of it as:

"Policy-as-code, but shipped as an attested runtime artifact."

Where Policy Bundles Sit in the Reference Architecture

flowchart TB
  subgraph Authoring["Policy Lifecycle"]
    A1["Policy-as-Code Repo<br/>(Git / PR reviews)"]
    A2["Approval Workflow<br/>(CISO/GRC/SecArch)"]
    A3["Policy Compiler<br/>+ Bundle Builder"]
    A4["Bundle Signing<br/>(KMS/HSM)"]
    A5["Policy Registry<br/>(Versioned Bundles)"]
    A1 --> A2 --> A3 --> A4 --> A5
  end

  subgraph Enforce["Runtime Enforcement"]
    E1["Gateway PEP"]
    E2["Sidecar PEP"]
  end

  subgraph Evidence["Evidence Pipeline"]
    V1["Decision Events<br/>(include policy_version + bundle_hash)"]
    V2["Evidence Pack Builder"]
    V3[("Immutable Evidence Store")]
    V1 --> V2 --> V3
  end

  A5 --> E1
  A5 --> E2

  E1 --> V1
  E2 --> V1

Key point: Every decision event includes:

policy_version
bundle_hash
(Optionally) signature_id

So the audit trail is provable.

The Minimum Fields a Bundle Should Contain

A practical Policy Bundle should contain:

Identity & Provenance

Field	Purpose
`bundle_id` + `version`	Unique identifier
`environment`	dev/test/prod
`created_at`, `approved_by`, `approved_at`	Accountability
`bundle_hash` + `signature`	Integrity

Controls

Field	Purpose
`control_id` + `name`	Identifier
`condition` (rule)	When to trigger
`action`	ALLOW/DENY/ESCALATE/TRANSFORM
`severity`/`risk_tier`	Enforcement intensity
`evidence_requirements`	What to log

Modes

Field	Purpose
`observe-only` vs `enforce`	Rollout safety
`default_deny` vs `default_escalate`	Unknown tool handling
`degraded_mode` behavior	Per risk tier fallbacks

Dependencies

Field	Purpose
`tool_registry_version`	Which tool constraints apply
`agent_registry_version`	Which agent permissions apply

Example: A Simple Bundle (Readable, Not Magical)

Below is a deliberately simple example. The power is not complexity—it's provenance + enforcement.

bundle_id: "fg-policy"
version: "1.0.3"
environment: "prod"
approved_by: ["ciso@company", "grc@company"]
created_at: "2026-01-09T00:00:00Z"
signature:
  key_id: "kms://fusegov/prod/policy-signing"
  bundle_hash: "sha256:3bf1..."
mode:
  default: "ENFORCE"
  unknown_tool: "DENY"
  degraded_mode:
    high_risk_tools: "ESCALATE"
    low_risk_tools: "ALLOW_WITH_LOG"
controls:
  - id: "CTL-001"
    name: "Tool allowlist + scope"
    when: "tool.id in tool_registry.approved && request.scope ⊆ tool.allowed_scopes"
    then: "ALLOW"
    evidence:
      required_fields: ["tool.id", "scope", "agent.id", "policy.version", "decision"]

  - id: "CTL-007"
    name: "Sensitive data boundary"
    when: "request.data_class > agent.allowed_data_class"
    then: "DENY"
    evidence:
      required_fields: ["data_class", "agent.allowed_data_class", "decision", "rationale"]

  - id: "CTL-010"
    name: "High-risk approvals"
    when: "tool.risk_tier in ['HIGH','CRITICAL'] && request.op in ['WRITE','DELETE']"
    then: "ESCALATE"
    approval:
      workflow: "ServiceNow"
      step_up_auth: true

A bundle like this is enough to establish:

Deterministic boundaries
Approval paths
Evidence model

Why Signing Matters (Even If You Trust Your Teams)

If you don't sign bundles, you can't prove:

Which policy ran at the time
Whether the runtime policy was tampered with
Whether a team hot-fixed controls without approval
Whether "shadow policies" existed

Signing + immutable storage gives you:

Benefit	What It Proves
Non-repudiation	"This was the approved control"
Tamper evidence	"This is the same bundle we deployed"
Audit defensibility	"Runtime matched approved policy"

Drift Detection: The Quiet Killer of Governance

In real enterprises, drift is inevitable:

A team bypasses the gateway for performance
A sidecar is misconfigured
A hotfix changes rules outside the change process
A tool is used before being registered

So governance needs drift detection that answers:

Are all enforcement points running approved bundle versions?
Are any environments out of date?
Are tools being called that aren't in the registry?
Are decisions being made without an evidence pack?

A Simple Drift Model

You can treat drift as three classes:

Drift Type	What It Means
Config drift	Deployed bundle version ≠ approved bundle version
Coverage drift	Tool calls not passing through a PEP
Inventory drift	Tools used that aren't registered/risk-tiered

Drift is not a "bug." It's a control gap—so it must be observable and reportable.

Rollback: The Safety Valve Enterprises Require

When you govern at runtime, mistakes can have consequences.

So your policy lifecycle must support safe rollback:

"Revert to last known good bundle"
"Disable semantic stage temporarily"
"Switch CRITICAL tools to escalate-only"

Rollback is how you keep governance from becoming the reason production breaks.

How to Pilot Policy Bundles (Fast and Safe)

If you want to adopt bundles without disruption:

Week 1 — Observe-only

Implement the bundle format
Deploy to gateway + sidecars
Log decisions with policy_version + bundle_hash

Week 2 — Enforce Top 3 Controls

Unknown tool = deny (or escalate)
Tool allowlist + scope bounding
High-risk tool approvals

Week 3 — Introduce Drift Reporting

Bundle version coverage report
Unknown tool usage report
Bypass/coverage gaps report

This produces immediate value even before "full governance."

The Takeaway

AI governance becomes real when you can say:

✅ These were the approved controls. ✅ This is how they were enforced at runtime. ✅ Here is the evidence for every decision. ✅ Here is our drift and rollback posture.

That's what Policy Bundles provide: a deployable, attested unit of governance.

This post is part of the FuseGov Reference Architecture series. It connects directly to:

Hybrid Runtime Governance (Gateway + Sidecar + Evidence Pipeline)
The Tool Registry (Governing the Action Surface)
Control-to-Evidence Traceability (Audit-Ready Proof)

Frequently Asked Questions

What is runtime policy enforcement for AI agents?

It is the process of evaluating agent tool calls and actions against a set of versioned security policies in real-time, at the network layer, before they are executed.

Why do you need Policy Bundles?

Policy Bundles provide a deployable, versioned, and cryptographically signed artifact that ensures the exact same rules are enforced across all gateways and sidecars, providing a provable audit trail.

How does policy-as-code help AI safety?

By treating policies as code, organizations can use Git-based reviews, automated testing, and version control to ensure that AI safety rules are robust and haven't been tampered with.

Policy Bundles: Versioned, Signed Controls for Runtime AI Governance