AI AutoGRC registers every AI system you run, buy, or embed as a regulated asset, assesses the risks only AI has (bias, drift, hallucination, prompt injection, training data provenance), maps the findings to the EU AI Act, NIST AI RMF, and ISO/IEC 42001, and records every governance decision in a tamper-evident ledger an auditor can verify by recomputing the hashes.
It can record that a model exists and give it a risk rating. It cannot tell you whether that model drifted past its approved bias threshold last Tuesday, whether its training data had consent, or whether you are ready for Article 9 of the EU AI Act. Those are the questions regulators are now asking, and the compliance deadlines are already phasing in.
Risk-tier classification, conformity assessment, data governance, human oversight, and post-market monitoring obligations phase in through 2027 for high-risk systems.
MAP, MEASURE, MANAGE, GOVERN. Federal agencies and DoD suppliers are being pulled to it through OMB M-24-10 and contract clauses.
Consequential-decision AI, video interview analysis, and automated employment decision tools now carry notice, audit, and reporting duties.
Eight governance capabilities, one decision record. Humans approve everything consequential: the platform prepares, blocks, and documents, and your named approver decides.
Every model, agent, pipeline, and embedded AI capability cataloged and classified to EU AI Act risk tiers and NIST AI RMF functions, with a dependency graph that flags every downstream system when an upstream one takes an adverse finding.
Eight dimensions general GRC cannot score: bias and fairness, explainability, robustness, privacy leakage, safety, hallucination rate, adversarial vulnerability, and training data provenance. Every rating traces to the exact evidence hash that produced it.
One control evaluated once propagates to every framework that requires it. Gap analyses show met, partial, and missing per framework with remediation steps.
Pre-deployment checks block release until classification, risk evidence, provenance, and a named approver sign-off are in place. Post-deployment telemetry catches drift against approved baselines and queues a rollback recommendation for human approval.
Governance incidents route through configurable channels with escalation chains. Every response action binds the responder's identity into a SHA-256 verification chain that detects any after-the-fact edit.
Vendor risk across five dimensions, machine-readable governance contracts checked against observed vendor behavior, shadow AI tiering, and incident propagation across every system that consumed a compromised output.
Determines which jurisdictions' AI rules apply, detects conflicts between them, applies the strictest requirement globally, and presents the same governance record in each regulator's vocabulary.
Quality gates between AI agents: evidence grounding, hallucination indicators, schema checks, and least-authority enforcement, with a hash chain proving no intermediate output was altered.
Every classification, assessment, approval, and response lands in an append-only, hash-chained decision ledger. Board and assessor reports come straight from it.
AI AutoGRC registers itself as a governed AI asset, runs its own risk assessment and framework gap analysis with the same engines it applies to your systems, and includes that self-assessment in every governance package it delivers. Ask any other GRC vendor for their self-assessment.
EU AI Act (Articles 9 through 15, 43, 50, 72), NIST AI RMF (MAP, MEASURE, MANAGE, GOVERN), ISO/IEC 42001, Executive Order 14110 and OMB M-24-10, the Colorado AI Act, the Illinois AI Video Interview Act, and NYC Local Law 144. Cross-framework deduplication means one piece of governance work satisfies every framework that asks for it.
Start free. The check runs in your browser and shows your readiness on screen. When you are ready to govern for real, Continuous is month-to-month and priced by the size of your AI estate.
Checkout is opening soon. Request access and we set you up by email, self-service end to end.
Send your approximate AI system count and we reply with a quote by return email. Written agreement, no calls required.
No. AI AutoGRC governs AI systems themselves as regulated assets against AI-specific frameworks. It is a separate product from cross-framework security compliance coverage tools, including our own.
Not in this release. You register AI systems through the API or dashboard, including third-party APIs, embedded AI in your tools, and reported shadow AI. Classification, risk assessment, gap analysis, and governance run from there. Agent-based discovery is on the roadmap and delivered in engagements.
It scores the evidence you submit: your test results, evaluation metrics, and attestations, with explicit thresholds you can reproduce by hand. Every rating binds the hash of the exact evidence scored. Live testing of your models is engagement work, not a self-serve claim.
No. Consequential actions require a named human approval recorded in the decision ledger. There is no auto-approve mode. The one automatic behavior is fail-safe: if a critical incident goes unacknowledged past its escalation window, processing is contained and every stakeholder is notified.
No. Decision records and incident response records are hash-chained. An auditor recomputes the chain and mathematically verifies nothing was inserted, deleted, or edited after the fact.
Continuous includes 15 registered AI systems, which covers most mid-market AI estates. Past that, the Enterprise tier prices by the number of AI systems governed, starting at $150,000 per year. Count your models, agents, pipelines, and embedded vendor AI, email the number to hello@ai4aigrc.ai, and you get a quote by return email.
United States regions only. See the Data Processing Addendum and subprocessor list.