Agentic GRC Can't Audit Itself — Here's What Has to Fill the Gap

The Proposition Sounds Reasonable — Until You Push On It

The pitch for agentic GRC platforms is coherent on the surface. Your compliance team is under-resourced. Evidence collection is manual, slow, and error-prone. A platform that continuously monitors controls, maps evidence to framework requirements, and flags drift in near real time sounds like exactly what a VP of Risk needs heading into an audit cycle. Vanta, recognized in the Forrester Wave: GRC Platforms, Q1 2025, represents a class of tooling that has genuinely matured — and for conventional IT compliance coverage across SOC 2 or ISO 27001, the productivity gains are real and defensible.

But something changes when you point that same class of tooling at AI systems — particularly the large language models, decision-support agents, and scoring models that are now entering production in BFSI and insurance at scale. The moment your GRC platform uses AI-driven agents to audit an AI model, you have introduced a structural circularity that no dashboard can dissolve: the auditor and the auditee share the same class of failure modes.

What Agentic GRC Actually Does Well

Before the critique, precision is warranted. AI GRC automation for regulated industries is genuinely useful for a defined set of tasks. Continuous control monitoring — checking whether encryption policies are enforced, access logs are complete, model versioning records exist — is well within the competence of an agent-assisted platform. So is automated evidence aggregation: pulling artifacts from your MLOps pipeline, your model registry, and your change management system and mapping them against ISO 42001 Clause 9 or an internal AI risk taxonomy. These are essentially retrieval and classification tasks, and agentic systems execute them with speed and consistency that human analysts cannot match at scale.

The table below draws the boundary that too many procurement conversations leave implicit.

Agent-Assisted GRC Tasks vs. Tasks Requiring Independent Adversarial QE

Task | Agent-Assisted GRC Appropriate | Requires Independent Adversarial QE Continuous control evidence collection | Yes | No Policy-to-control gap mapping | Yes | No Model documentation completeness check | Yes | No Drift detection against baseline metrics | Yes — flags, does not adjudicate | Adjudication and root-cause analysis Bias and fairness evaluation across protected classes | Partial — known test sets only | Yes — novel adversarial probes required Red-teaming for prompt injection and jailbreak | No | Yes Audit-trail integrity for regulator submission | No — cannot attest to its own outputs | Yes — requires human-signed, independent attestation ISO 42001 / EU AI Act conformance assessment | Preparatory only | Yes — independent assessment required by the standards Model behavior under distribution shift | No | Yes

The right-hand column is not where agentic GRC platforms are heading with a few more product cycles. It is where they are structurally incapable of going — not because of immaturity, but because of what independence means in audit and assurance contexts.

The Independence Problem Is Regulatory, Not Philosophical

ISO 42001, the international management system standard for AI, requires that organizations establish and maintain independent review mechanisms for their AI systems. The EU AI Act, for high-risk AI applications in credit, insurance underwriting, and claims processing, mandates conformance assessment and technical documentation that demonstrates the system behaves as intended across its operational range. RBI's model risk management guidelines and SEBI's recent AI governance advisories both carry expectations of independent model validation that predate the current wave of agentic tooling.

None of these frameworks accept self-attestation by the system under review. An agentic GRC platform that flags its own AI-driven monitoring agent as compliant has produced, at best, a consistency check — not an independent assessment. Regulators examining your AI governance posture will draw that distinction quickly, and the consequences of that gap showing up in an examination rather than in your own testing program are severe.

Where the Blind Spot Lives

The deeper problem is epistemological. An AI-driven GRC agent evaluates what it can observe, using the representations its underlying model has learned to recognize as relevant. If that model was trained predominantly on conventional IT compliance evidence — the bulk of the training signal for any GRC platform to date — its coverage of novel AI failure modes is unverified and almost certainly incomplete. It will not find the adversarial prompt that causes your lending decisioning model to produce discriminatory outputs at a specific input length. It will not detect the confidence miscalibration that makes your fraud model overconfident on a demographic segment it rarely encountered during training. It will not surface the latent data pipeline assumption that causes your model to behave correctly in testing and incorrectly at the tail of the production distribution.

Those failure modes are not exotic. They are the failure modes that have produced regulatory enforcement actions in BFSI and insurance globally over the past four years. The fact that your GRC dashboard shows green on model monitoring does not mean the model is safe. It means the monitoring agent found nothing it was designed to look for.

The Structural Answer: Separation of Assurance Functions

The organizational response is not to distrust agentic GRC tooling — it is to scope it correctly. Treat AI GRC automation for regulated industries as your continuous compliance operations layer: fast, scalable, useful for maintaining audit-readiness and surfacing known-class issues. Then treat independent adversarial QE as a separate assurance function with its own mandate, its own methodology, and its own reporting line — one that does not share tooling, test sets, or governance assumptions with the platform it is evaluating.

This is not a novel idea. It is exactly the separation that financial services firms have maintained between first-line model development, second-line model risk management, and third-line internal audit for decades. Agentic GRC fits in the second line. Adversarial QE, conducted by a team or partner that has no stake in the model's performance or the platform's health metrics, occupies the third-line role. Collapsing those two functions into a single automated platform does not eliminate the assurance gap — it makes it invisible.

Three Things to Do Before Your Next Audit Cycle

First, audit your current GRC platform's AI-specific coverage. Ask your vendor to show you specifically which ISO 42001 controls their agents evaluate, using what evidence sources, and with what known limitations on AI system types. If they cannot answer that question with specificity, your compliance posture for AI systems is thinner than your dashboard suggests.

Second, establish an explicit adversarial testing mandate that sits outside your GRC toolchain. This means documented red-teaming protocols, independent test data that is not drawn from the same distribution as your model's training or validation sets, and a reporting chain that terminates at a risk or audit committee — not at the AI product team.

Third, map your regulatory obligations to assurance methods, not to tools. For every AI system in scope under EU AI Act, ISO 42001, or applicable RBI or SEBI guidance, identify which obligations require independent human attestation and which can be satisfied by automated evidence. That mapping should drive your investment decisions — and it will reveal that the independent adversarial testing budget is not overhead. It is the part of your compliance program that the automated layer literally cannot perform for itself.

The goal of AI assurance is not a compliant dashboard. It is a defensible, evidence-backed account of how your AI systems behave, where they fail, and what you have done about it. Agentic GRC gets you partway there. The rest requires a function that the platform cannot replace.