AI AutoGRC registers every AI system you run, buy, or embed as a regulated asset, assesses the risks only AI has (bias and fairness, explainability, robustness, privacy leakage, safety, hallucination, adversarial vulnerability, 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's disparate impact ratio clears your threshold, 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. The voluntary US framework for managing AI risk, published by NIST as AI 100-1.
Consequential-decision AI, video interview analysis, and automated employment decision tools now carry notice, audit, and reporting duties.
One decision record across every capability. 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.
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. A blocked release names every item standing in the way.
Governance incidents notify your escalation chain by an entry in your incident record and by email, advancing to the next contact when nobody acknowledges. Each incident action is recorded in your SHA-256 hash-chained ledger, and an acknowledgement records the responder named when it was made.
Classifications, assessments, approvals and incident actions are recorded in a hash-chained decision ledger, and each monthly report carries its entry count and latest hash.
These run on the same engine and the same decision ledger, operated by us rather than from your own portal. Post-deployment drift detection against approved baselines, and rollback recommendations for human approval, run the same way. They are not part of self-serve Continuous today. Write hello@ai4aigrc.ai for scope and pricing.
Vendor risk across five dimensions, machine-readable governance contracts checked against observed vendor behavior, shadow AI tiering, a dependency graph that flags every downstream system when an upstream one takes an adverse finding, 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.
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, OMB M-25-21 minimum practices for federal high-impact AI, the Colorado AI Act, the Illinois AI Video Interview Act, and NYC Local Law 144. Cross-framework deduplication evaluates each control once and reports the result against every catalog requirement that maps to 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.
Self-service checkout through Stripe. Prefer email? Write hello@ai4aigrc.ai and we set you up that way.
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 by replying to your onboarding email with your inventory, 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 notification: if a critical incident goes unacknowledged past its window, or a serious one reaches the end of your escalation list unacknowledged, the incident is marked contained in your ledger and every stakeholder on your escalation list is notified. Containing the processing itself is your call to make in your own systems; the platform has no reach into them.
No. Decision records and incident response records are hash-chained, and each entry is signed. An auditor recomputes the hash chain, which exposes an entry edited in place and any gap in the sequence. Each entry also carries a signature made with our server key, which our own verification checks to catch a rewritten chain. One limit we state rather than paper over: a chain cannot by itself prove nothing was removed from the very end, so every verification reports the chain length and the first unsigned entry, and every monthly report records the entry count and the latest entry's hash. That email is your own copy: a later verification showing fewer entries, or a different hash at the same entry, means entries were removed.
Continuous includes 25 registered AI systems, which covers most mid-market AI estates. Past that, the Enterprise tier is priced by the number of AI systems governed. 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.