Your function is on the third line. The work has changed. Agentic AI takes high-risk actions thousands of times a day, faster than any sample-based audit plan can keep up with. Reign was built so risk-based audit planning, continuous control monitoring, and audit-grade evidence travel together at the speed the business now operates.
Internal Audit has always tested whether controls were designed and operating effectively. Agentic AI has split that test into two distinct questions, asked thousands of times per day, on a clock the audit plan was never designed for.
“This is the control activity. Policy at the point of decision. Identity bound. Risk classified. Logged the moment the decision is made, not reconstructed at exam time.”
“This is the test of operating effectiveness. Outcome compared to intent. Variance captured as evidence. The work the third line has always done, now running continuously instead of quarterly.”
Reign instruments both questions for every agent, every model invocation, every tool call. The audit plan inherits a continuous, population-level evidence chain instead of a quarterly snapshot.
The IIA’s risk-based audit plan was designed for transaction populations that move at quarterly cadence. Agentic AI now drives credit decisions, payment approvals, vendor onboarding, IT remediation, security containment, and customer entitlements in the same operating day. Each one is a control activity. Each one is in scope.
Sample-based testing cannot defensibly cover a population of that size. Application logs are not control evidence. The cycle mismatch between AI deployment and audit prep is widening, and the third line is the function that carries the residual. Reign closes the gap by making the evidence chain continuous and complete at the population level.
CAVR is the foundation Internal Audit has always tested for IT general controls. Continuous Audit Validation Reporting makes the same four assertions hold up at the speed and scale agentic AI operates.
Every model call, every agent action, every tool invocation captured at the gateway and written to the audit chain. No gaps because every action goes through Reign before it reaches a system of record.
Tamper-evident evidence with ALCOA+ data integrity attributes. Attributable. Legible. Contemporaneous. Original. Accurate. Plus complete, consistent, enduring, and available. Auditor-verifiable in minutes.
Policy at the decision point. Every approval and every denial is part of the audit chain. Unauthorized actions are blocked before execution rather than reconstructed afterward.
Identity-bound to every AI call. Segregation of duties enforced in the runtime. The same person cannot both develop and approve a model change. Every access decision logged and queryable.
Most AI governance tooling produces one dashboard for one persona. That structure does not survive an examination. The second line cannot independently challenge a control it cannot see. The third line cannot provide independent assurance on evidence it does not own. Reign was architected against the Three Lines model from day one. Same evidence chain. Different views, different access controls, different working-paper trails per line.
Reign surfaces the highest-frequency, highest-impact agent actions across the enterprise so the audit universe reflects how AI actually operates today, not how it was scoped in last year’s plan.
Anomaly detection runs against the evidence chain in real time, with materiality thresholds set by your function. Findings that cross threshold escalate to the CAE, to the Chief Risk Officer, and into the Audit Committee dashboard with full context attached.
Population-level test packets generated on demand. Test-of-design and test-of-operating-effectiveness artifacts assembled from the same ledger the first line operates against, walkthrough-ready for the external audit firm.
Verifiable, reproducible artifacts. The same query against the same evidence corpus produces an identical artifact, with cryptographic chain of custody. The external auditor walks away with population-level evidence instead of a sample.
A five-stage maturity model for AI inside Internal Audit. Stage one is unscoped, AI in production with no audit coverage. Stage five is continuous, AI fully scoped, continuously monitored, and assurance-ready on demand. Most functions today sit between stage two and stage three.
The gated download walks through where your function sits, what changes at each stage, and the order to sequence the work.
Download the AI Audit Maturity Model (PDF)Four stages. Each one scopes and qualifies the next. Most CAE-led functions start at Stage 1 with an Executive Assurance Briefing, then move into a Runtime Risk and Governance Assessment scoped to the agents and workflows already on the audit plan.
Four stages. Briefing scopes the question. Assessment scopes the surface area. Pilot proves runtime evidence at a fixed scope. Platform Rollout is where Reign goes into steady-state production. Most CAE-led functions start at Stage 1.