A six-pillar governance model for securing, controlling, and auditing autonomous AI agents in real enterprise environments.
Built for systems that can take actions, use tools, persist memory, and pursue goals over time — not static chatbots.
Every agent is attributable to a unique identity and explicitly bound to an approved purpose and scope.
Runtime guardrails enforce what agents can and can’t do — especially around tools, data, and permissions.
Detects deviations from expected behavior, role, or operating boundaries before the drift becomes impact.
Decision trails, tool calls, and state transitions are visible and reviewable for audit and forensics.
Fault isolation, rollback, and kill switches reduce blast radius when agents misbehave or are compromised.
Humans retain decision rights, escalation paths, and override authority — especially for high-impact actions.
The ACR Framework™ defines foundational mechanisms to govern the safe, responsible, and auditable operation of agentic AI systems. Together, they create a layered architecture for control, containment, and human decision authority.
ACR applies to autonomous or semi-autonomous AI agents that can take actions, use tools/APIs, access data, persist memory, or pursue goals across sessions. It is designed to complement (not replace) existing security, risk, and compliance programs by making agentic systems implementable and auditable.
Every agent must be attributable to a unique identity and explicitly bound to an approved purpose, scope, and owner — preventing unauthorized repurposing and unclear accountability.
Objective: Every action is attributable and purpose-bound (who did what, on whose authority, and for what approved intent).
Mechanisms:
Evidence:
Dynamic runtime policies define what an agent is allowed to do — and enforce it continuously at execution time (not just in documentation).
Objective: High-risk behaviors are blocked or gated in real time, even when the model “wants” to do them.
Mechanisms:
Evidence:
Detects when an agent begins to deviate from its intended role, constraints, or operating patterns — enabling early alerts and corrective actions before harm occurs.
Objective: Detect “it’s going off-script” early — not after an incident report.
Mechanisms:
Evidence:
Ensures complete transparency into what the agent did, when, and why — including tool calls, policy decisions, and state transitions. Observability is the foundation of auditability and trust.
Objective: Reconstruct an agent’s decision and execution path end-to-end (prompt → tools → actions → outcomes).
Mechanisms:
Evidence:
Agents must recognize faults, isolate compromised behavior, revert to safe states, or shut down. Containment reduces blast radius when risk is detected — whether from bugs, misuse, or compromise.
Objective: When things go wrong, the system fails safely and limits impact.
Mechanisms:
Evidence:
Human operators retain the ability to monitor, intervene, and override. Oversight defines decision rights: what must be reviewed, who approves, and how exceptions are handled.
Objective: Humans keep authority over high-impact outcomes, with clear escalation and accountability.
Mechanisms:
Evidence:
Interested in contributing to the development of the ACR Framework™?
Join the Contributor NetworkAI is entering a new phase — one where agents can take actions, use tools, access data, and operate across time. This power requires governance that works at runtime, not just at review time.
Traditional security and compliance programs assume software is deterministic and bounded. Agentic systems are not: they are goal-seeking, probabilistic, and capable of drift. ACR defines practical mechanisms to keep these systems controlled, auditable, and aligned to human decision authority.
ACR is designed to be implemented in real enterprise environments — with controls that can be tested, measured, and evidenced.
Creator of the ACR Framework™
Adam is a cybersecurity leader focused on AI governance, alignment, and resilience. He created the ACR Framework™ to help organizations implement agentic AI with controls that are practical, testable, and auditable.
Want to pressure-test your agent roadmap against a concrete governance model? ACR is designed to be implementable with existing security and GRC programs — and to produce evidence leaders can defend.
The ACR Framework™ is just the beginning. We’re building a community of engineers, researchers, and leaders who care about safety, accountability, and implementable control of autonomous systems.