Your end-to-end platform for trustworthy AI — unifying governance (policy, compliance, frameworks) with risk management (assessment, quantified scoring, controls and continuous monitoring). Free, private, and running the moment you open it.
No sign-up · Core risk analysis runs locally in your browser
Your end-to-end partner for trustworthy AI. autogovern.io unifies AI governance — policy, compliance and framework design — with AI risk management — risk assessment, quantified scoring, controls and continuous monitoring — for organizations big or small.
Your fairness, drift & risk analysis runs locally in your browser — we never see that data. AI-assisted features like agent-guided fixes are clearly labeled so you always know where your content goes. No sign-up.
Local-only core engine
Jurisdictions covered
Framework aligned
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We read eleven AI-governance platforms for this. Every one of them gates its real product behind a demo request. Ours doesn't — you just ran a real risk check above, and nobody asked for your email first.
"Free" usually means a glossary, a checklist, or a gated report. The actual assessment sits behind a sales conversation.
The Governance Workbench is the actual product — assessment, controls, runtime governance, monitoring and assurance, free and private, running the moment you open it. No call required.
People mix these two up all the time. Here's the difference — no jargon. Click through the story.
Think of your AI as a ship setting sail. It's powerful and fast — and it's carrying real people's outcomes: loans approved, patients triaged, résumés screened. The question isn't just can it sail. It's: who's steering, and what happens when the sea turns rough?
Those two questions are AI Governance and AI Risk Management. Let's meet them.
Governance is direction and accountability. It decides where the ship is going, who is in command, and what the rules are. Without it, you have a powerful ship with no one at the wheel — drifting wherever the current takes it.
Risk management is discipline and control. It watches the water for icebergs, checks the hull, and keeps the lifeboats ready. Without it, even a well-commanded ship sails confidently… straight into the storm it never saw.
Governance sets the course and names who's responsible. Risk management watches the water and keeps everyone safe. One is direction; the other is discipline. You need both to get your AI — and the people it affects — safely to shore.
That's what we help you build — the captain and the radar.
The framework of policies, processes and tools an organization uses to keep its AI systems fair, secure, transparent and compliant with global legislation.
AI Governance is the framework of policies, processes, and tools implemented by an organization to ensure its artificial intelligence systems are fair, secure, transparent, and compliant with global legislation.
Governance decides who's accountable. Risk management finds out what actually goes wrong. Most platforms sell you one and call it the other — we build both, and keep them separate on purpose, because a policy nobody tested and a risk nobody owns are the same failure wearing different names.
Adhering to regulatory frameworks before systems are audited or penalised.
Scanning input parameters to prevent algorithmic discrimination.
Guarding against concept drift, data leakage, and adversarial vulnerability.
As global regulations evolve, understanding compliance requirements is key to avoiding liabilities and scaling trustworthy models. Click on the frameworks below to see details.
Status: Legally enacted as Regulation (EU) 2024/1689 — the world's first comprehensive horizontal AI law. It applies in phases: prohibited practices since 2 Feb 2025, general-purpose AI (GPAI) obligations since 2 Aug 2025, Art. 50 transparency duties from 2 Aug 2026, the full Annex III high-risk regime from 2 Dec 2027, and product-embedded (Annex I) high-risk AI from 2 Aug 2028. The two later high-risk dates were pushed back by the Digital Omnibus (adopted by the Parliament and Council in June 2026, pending Official Journal publication). Penalties reach €35M or 7% of global annual turnover.
Status: Voluntary framework — v1.0 (Jan 2023) plus a Generative AI Profile (NIST AI 600-1, July 2024). Widely adopted across US government and industry, and the basis for the safe-harbour defence written into Texas's TRAIGA.
Status: Published Dec 2023 — the first certifiable AI Management System (AIMS) standard. Now backed by two 2025 companions: ISO/IEC 42005 (AI system impact assessment) and ISO/IEC 42006 (requirements for bodies that certify an AIMS).
Status: No AI-specific statute — the FTC applies its existing Section 5 authority (unfair or deceptive acts or practices) to AI, alongside consumer-protection and anti-discrimination law.
Status: With no comprehensive US federal AI statute, the states are setting the pace. Key dates we track:
Answer a few questions about your AI system. The tool classifies it under the EU AI Act, maps the frameworks that apply, lists your obligations, and scores your governance readiness — then builds a roadmap to close the gaps. Runs entirely in your browser; save it for a shareable link or download the report.
Fill in the factors above and select “Assess my system” to see your classification, obligations and readiness score.
Download detailed, board-ready AI Governance and AI Risk Management policy documents — fill in the bracketed placeholders and adopt them in your company. Free, no sign-up.
Boards are asking for AI policies. Executives are forming AI councils. Legal, compliance, security, privacy, data and technology teams are building governance playbooks. That is a good thing — but governance sets direction, while risk management creates the discipline that keeps AI within tolerance.
AI risk management is not a one-time model review, a checklist at launch, or a policy document sitting in a shared folder. It is a continuous capability across the full lifecycle. autogovern.io runs it end to end.
Surface AI-specific risks across data, model, security, people and third parties.
Score likelihood × impact, rank inherent vs. residual risk, visualise a heat map.
Apply controls — mitigate, transfer, avoid or knowingly accept — with named owners.
Track key risk indicators, drift and incidents so residual risk stays in appetite.
AI is moving from “assistive” to “agentic.” Traditional AI tools produced outputs. AI agents take actions — they search, summarize, write, classify, route, trigger workflows, update systems, call APIs, interact with customers, make recommendations, and in some cases execute decisions at machine speed. That changes the risk profile.
A chatbot giving a wrong answer is a quality issue.
An agent taking the wrong action in a production workflow can become an operational, legal, financial, cybersecurity, reputational or customer-harm issue.
An agent is not just a model. It is a system of models, tools, data, permissions, memory, APIs, workflows and humans. The risk is not only in the model output — it is in the action path.
An agent that's wrong is a bug. An agent that's wrong and acts on it is an incident. Most governance tools review outputs after the fact — our Agent Control Plane governs the action path itself, in real time, with every decision sealed into a tamper-evident ledger.
If you can’t answer these, the agent is operating outside of risk tolerance.
Check the box wherever the answer is “yes, at least one agent.” Your exposure score updates as you go.
A continuous loop, not a one-off project — seven stages that take a use case from first framing to enterprise-wide oversight, each mapped to the functions of the NIST AI Risk Management Framework.
Understand what the AI actually does — advising, deciding, executing, escalating, monitoring, or interacting with external parties. The risk depends on the role AI plays.
NIST: MAPNot every use case needs the same oversight. A marketing draft assistant is not an agent handling regulated decisions, financial approvals, medical triage, hiring, fraud review or cyber response.
NIST: MAPAI risk is not only hallucination — it includes bias, privacy leakage, data poisoning, prompt injection, drift, overreliance, unauthorized tool use, weak oversight and unclear accountability.
NIST: MEASUREDecide what level of error, autonomy, exposure and uncertainty is acceptable. Without thresholds, teams can’t tell a manageable risk from a stop-the-line risk.
NIST: GOVERNHuman-in-the-loop review, least-privilege access, approval gates, logging, monitoring, red teaming, testing, escalation paths, fallback procedures and clear ownership.
NIST: MANAGEAI risk is dynamic. Models, prompts, data, users, threat actors and business processes all change. A risk assessment done once at launch will not be enough.
NIST: MEASURE · MANAGEAI risk should not live in a silo. Connect it to operational, third-party, cyber, compliance, privacy and model risk, business continuity, audit and board reporting.
NIST: GOVERNPick the maturity of each stage in your organization today — your overall lifecycle maturity updates as you go.
You can’t manage what you can’t name. We assess every system against thirteen categories of AI risk — and a concrete library of the ways AI actually fails.
“AI risk” is far broader than hallucination. Tap any you’ve actually seen or tested for — each is a concrete failure we test and monitor for.
Score a risk the way we do in an engagement. Pick a category, rate likelihood and impact, and add controls — the tool computes inherent & residual risk and recommends a treatment. Runs entirely in your browser.
Reduce likelihood or impact with additional controls before deployment.
Educational risk-triage aid — not legal advice. For a defensible, audit-ready assessment, run your system through the Governance Workbench or book an engagement.
Every risk you add above lands here. The register scores each risk’s inherent and residual exposure, recommends a treatment, and rolls them up into an overall posture for the system — ready to save, share, or download as a board-ready report.
Have both a saved Governance Dossier and a saved Risk Register? Paste their share links (or bare IDs) to generate one board-ready briefing covering both.
Our methodology is grounded in the recognised AI and enterprise risk standards — so your program is portable, auditable and defensible.
Status: Voluntary US framework — v1.0 (Jan 2023) with a 2024 Generative AI Profile (NIST AI 600-1). The de-facto baseline for AI risk programs.
Status: International standard giving AI-specific guidance on applying ISO 31000 risk management to artificial intelligence.
Status: The global parent standard for risk management of any kind.
Status: Legally enacted (Regulation (EU) 2024/1689). Article 9 mandates a continuous risk-management system across the lifecycle of high-risk AI; the full high-risk regime applies from 2 Dec 2027 (moved by the Digital Omnibus).
Status: Industry security checklist for generative-AI applications.
Status: A living, peer-reviewed database of 1,000+ documented AI risks.
Set an adoption status for each framework you follow — your coverage score updates as you go.
Every risk on the register gets an explicit, owned decision — and a control that brings residual risk within a defined appetite. No risk is left undecided.
Apply controls to reduce likelihood or impact — testing, guardrails, human oversight, monitoring.
Shift the risk via insurance, contractual indemnities, or vendor SLAs and warranties.
Stop or redesign the use case when residual risk exceeds appetite and can’t be reduced.
Knowingly accept low residual risk — documented, owned and signed off, with a review date.
Organizations must decide what level of error, autonomy, exposure and uncertainty is acceptable. Without thresholds, teams cannot tell the difference between a manageable risk and a stop-the-line risk.
Mandatory review on consequential or irreversible actions.
Agents get the minimum data, tools and permissions they need.
Explicit sign-off before high-impact actions execute.
Every decision and action is recorded and reconstructable.
Real-time KRIs flag drift, breaches and anomalies.
Adversarial testing against misuse, abuse and failure scenarios.
Clear routes to stop, escalate or redesign when thresholds trip.
Safe defaults and the ability to halt an agent immediately.
Risk doesn’t stand still. We instrument the metrics that tell you when residual risk is drifting out of appetite.
Population stability & KS divergence between baseline and live data.
4/5ths disparate-impact ratio across protected groups.
Live performance vs. validation baseline, with decay alerts.
Open serious-incident count and mean time to remediate.
Jailbreak / prompt-injection / unsafe-output rate for LLM systems.
Share of high-risk systems with controls implemented & tested.
Accountability that is unambiguous, connected to enterprise risk, and built to mature — so you can scale AI safely, reliably and responsibly.
Product, data-science & engineering teams who build and run AI — they own the risk day to day.
AI risk, compliance & ethics functions setting policy, the risk appetite and the register.
Internal audit & independent review providing assurance to the board and regulators.
AI risk should not live in a silo. It belongs in the systems you already use to run the business.
Tap the ones already connected to your AI risk process today.
The common gap: governance forums without the risk discipline underneath them.
Check any that describe your organization today.
It will not be defined by who adopts AI fastest — but by who can scale AI safely, reliably and responsibly. The winners won’t just have AI governance committees; they will have AI risk management capabilities. They will know:
Check off the ones your organization can answer with confidence today:
Engagements that take you from zero to a living, board-ready AI risk program.
System-by-system identification & scoring workshops producing a prioritised, inherent-vs-residual risk view.
A living risk register with owners, treatments, controls, due dates and residual-risk tracking.
Model validation, challenge and lifecycle controls per the interagency guidance (SR 26-2, successor to SR 11-7), adapted for ML & foundation models.
Vendor & foundation-model due diligence, plus OWASP-LLM red-teaming for your generative-AI stack.
Stand up KRIs, drift & fairness dashboards and an incident-response playbook for live AI.
Translate the register into the top-risk dashboards, appetite statements and trends your board needs.
The question is no longer, “Do we have an AI policy?”
“Do we understand, measure, monitor and manage the risks created by the AI systems and agents we are putting into the business?”
That is where the real work begins — and where autogovern.io partners with you.
Run a real, private assessment in the Governance Workbench, or book a risk-strategy briefing with our team.
Our autonomous AI Agents crawl global channels, catalog security incidents, generate real-time safety analyses, and answer visitor questions.
Scrapes global news feeds, arXiv disclosures, and code repos for emerging AI failures and vulnerability payloads.
Performs root-cause analysis on AI incident payloads and drafts mitigation playbooks and blog briefs.
Engages with customers in real-time, resolving regulatory compliance queries and explaining AutoGovern features.
AI Q&A Agent • Online
A live database of real-world AI governance & risk incidents — ingested continuously from public news feeds, auto-classified by category & severity, and analyzed for the governance controls that would have prevented them.
Every day our AI agents publish two fresh, technical posts — one on AI Governance, one on AI Risk Management — drawn from real incidents in the news, sharp industry debates, and the occasional bold new idea.
From agile startups deploying lightweight APIs to global enterprises running hundreds of proprietary neural networks, our services scale with your needs.
Evaluate your model's regulatory risk profile in real-time or simulate live model metric monitoring. Choose an application tab below to start.
A single, standalone binary that finds real misconfigurations across your environment — Windows, macOS, Linux, and cloud. Download it, run it, get a prioritized report in seconds. No installer, no account, no data leaves your machine.
FileVault, firewall, Gatekeeper, SIP, remote login, exposed ports.
Firewall profiles, Defender, SMBv1, RDP NLA, UAC, Guest account, BitLocker.
SSH hardening, shadow perms, sudo, firewall, patch backlog, listeners.
# macOS / Linux
chmod +x aegis-* # make it executable
./aegis-* # scan this host, human-readable report
./aegis-* scan -format html -output report.html # shareable HTML
./aegis-* scan -fail-on high # CI gate (exit 1)# Windows (PowerShell)
.\aegis-windows-amd64.exe # scan this host
.\aegis-windows-amd64.exe scan -format sarif -output aegis.sarifTip: run with sudo / an elevated shell for full coverage of protected files. Cloud collectors (AWS · Azure · GCP) are coming next.
A standalone CLI that answers the question your CVE list can't: which of these are actually being exploited right now? Pulse re-ranks your existing Trivy, Grype or npm-audit report — or scans a path directly — against live CISA KEV and EPSS exploitation data, so you fix the five that matter instead of the five hundred that don't.
pulse update (or passing --online) downloads the latest public CISA-KEV/EPSS exploitation dataset so your offline triage stays current. That call carries no scan results, file paths, or identifying data — it's a one-way download of public threat intel, never an upload of anything from your machine.
Apple Silicon and Intel builds — scan a path or triage an existing SCA report.
Scan a container image root, a repo checkout, or the whole host.
# 1. Scan a path (or the whole host) directly
chmod +x pulse-*
./pulse-* scan . # scan this directory
./pulse-* scan / --fail-on kev # CI gate: fail only on known-exploited CVEs# 2. Triage a report you already have (Trivy / Grype / npm-audit)
trivy image myapp:latest --format json --output trivy.json
./pulse-* triage trivy.json --json # re-ranked by real-world exploitationFindings are ranked against CISA's Known Exploited Vulnerabilities (KEV) catalog and EPSS exploit-probability scores. pulse update refreshes the offline snapshot; add --online to any scan/triage to refresh first.
Whether you need a quick EU AI Act readiness audit or a fully customized enterprise-wide governance strategy, our advisory team is ready to guide you.
governance@autogovern.io
Run your assessment now — no sign-up needed