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Choose your path through AI governance.

Start with the tools that fit your role. All existing tools remain available from the menus and tool search.

AI GOVERNANCE & RISK MANAGEMENT

AI moves fast.
Trust needs
evidence.

Know what AI you have. Understand its risks.
Bring the evidence behind every decision into view.

Open the workbench Explore evidence rooms

Core risk analysis runs in your browser. Saving and sharing are under your control.

FROM AI ACTIVITY TO ACCOUNTABILITY

Connect source evidenceDiscovery & integrations
AIYOUR GOVERNANCE WORKFLOW

Your AI system

A clear view of the system.
A traceable path to the proof.

Accountable ownerHuman review and judgment
Discover → assess → prove
Review changed evidenceKnow what needs attention
Customer evidence roomSelected artifacts · controlled access

ILLUSTRATIVE WORKFLOW · NOT LIVE WORKSPACE DATA

A shared language
for responsible AI.

NIST AI RMFISO/IEC 42001EU AI ACT

Framework guidance.
Evidence-backed decisions.

YOUR NEXT STEP

From discovery to proof.

Platform user guide ↗
01 / DISCOVER

Know what’s in use.

Connect GitHub source evidence and review discoveries before importing them.

 02 / UNDERSTAND

See the work ahead.

Assess your system, map risks and work through the controls and supporting evidence.

 03 / PROVE

Share the right proof.

Prepare a customer evidence room with selected artifacts and clear sharing controls.
AI Governance + Risk Management

Keep the next decision in view.

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.

Launch the Governance Workbench → Download Free Security Scanner
38 Every AI law we track, in plain English 38 regulations · 91 requirements · the controls that satisfy each one, and the real incidents where they failed → ▶ New to AI Governance & Risk? Watch the 2-minute animated story — Governance vs. Risk, explained simply →

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.

100%

Local-only core engine

16+

Jurisdictions covered

ISO 42001

Framework aligned

AutoGovern Continuous Guardrails
Live incident Loading real AI incidents…
Latest from the blog Blog

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Next regulatory deadline —

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    Every regulation, with its requirements →

    60-second risk check Not checked

      Quick estimate, not a legal classification. Full assessment →

      Why autogovern.io

      You shouldn't need a sales call to find out if you have a problem

      Start with an assessment, inspect the supporting evidence, and decide what to share. The core workbench is available to explore without a sales call.

      Understand

      Bring the questions into focus

      Use framework guidance, risk assessments and control evidence to identify the work your AI system needs.

      autogovern.io

      Run it right now — the real product, not a preview

      Explore assessment, controls, monitoring and assurance. Core risk analysis runs in your browser; saving, connected evidence and sharing have their own explicit controls.

      Launch the Governance Workbench →
      Framework landscape

      72 frameworks tracked. 38 your systems can be measured against.

      Most platforms publish one number for “frameworks supported”, which quietly mixes two different things: instruments they can actually check your system against, and instruments they have heard of. We separate them, and show you which is which.

      38
      Rules mapped

      A full entry in the regulation catalogue, with executable requirements this platform evaluates your systems against.

      3
      Used elsewhere

      Used by the platform outside the regulation catalogue — in the risk methodology or the threat matrix — but not as a rules-carrying entry.

      3
      Named, not separate

      Named inside another framework's description rather than tracked as its own assessable instrument.

      28
      Tracked, not mapped

      Part of the tracked landscape with no rule mapping yet. Not a claim that nothing internally maps to it.

      Binding law & regulation

      23

      Enforceable statutes, regulations and supervisory rules.

      • EU Artificial Intelligence Act (Regulation (EU) 2024/1689)
      • General Data Protection Regulation (GDPR)
      • Illinois Biometric Information Privacy Act (BIPA)
      • Illinois HB 3773 — AI in Employment (IHRA amendment)
      • Brazilian AI Bill (PL 2338/2023)
      • + 18 more

      ISO/IEC standards

      10

      The international standards family — management systems, risk, impact assessment, lifecycle, data quality and bias.

      • ISO/IEC 23894:2023 — AI Risk Management
      • ISO/IEC 42001:2023 — AI Management System (AIMS)
      • ISO 31000:2018 — Risk Management Guidelines
      • ISO/IEC 38507:2022 — Governance implications of AI
      • ISO/IEC 42005:2025 — AI System Impact Assessment
      • + 5 more

      NIST

      2

      The US voluntary framework family, including the generative-AI profile.

      • NIST AI Risk Management Framework 1.0
      • NIST AI 600-1 — Generative AI Profile

      IEEE standards

      3

      Ethical design, transparency and algorithmic-bias engineering standards.

      • IEEE/ISO/IEC 24748-7000:2022 — Ethical concerns during syste…
      • IEEE 7001-2021 — Transparency of Autonomous Systems
      • IEEE 7003-2024 — Algorithmic Bias Considerations

      International principles & treaties

      8

      Intergovernmental principles, treaty frameworks and multilateral codes.

      • OECD AI Principles
      • UNESCO Recommendation on the Ethics of Artificial Intelligence
      • Council of Europe Framework Convention on AI, Human Rights, …
      • Council of Europe HUDERIA Methodology
      • G7 Hiroshima AI Process Comprehensive Policy Framework / Cod…
      • + 3 more

      National frameworks & guidance

      24

      Country-level voluntary frameworks, guidance and public-sector policy.

      • Canada Voluntary Code of Conduct on Advanced Generative AI
      • Artificial Intelligence and Data Act (AIDA, Bill C-27) — lap…
      • OSFI Guideline E-23 — Model Risk Management
      • Canadian AI Standards (CSA ISO/IEC 42001 & 23894, CAN/DGSI 1…
      • EU Financial Supervisors AI Guidance (EBA, ESMA, EIOPA)
      • + 19 more

      Security & risk taxonomies

      2

      Application-security risk lists and research risk repositories.

      • OWASP Top 10 for LLM / GenAI Applications
      • MIT AI Risk Repository

      Rules mapped   Used elsewhere   Named, not separate   Tracked, not mapped

      Compare all 72 frameworks → See the mapped requirements
      Governance Effectiveness Score

      Most governance scores measure activity. This one measures proof.

      Policies written, meetings held, training delivered — none of it proves an organization is governed. Eight factors do: whether you can see risk, name an owner, verify controls, produce evidence, and respond before damage spreads. Multiplied, not averaged, so a strong factor cannot hide a weak one.

      G = 100 × C × (V0.15 × O0.20 × K0.25 × E0.15 × R0.15 × I0.10) × (1 − P) G v1.1
      C
      Coverage How much of your critical systems, data, vendors, processes, and AI use cases are inside governance.
      V
      Visibility Can you see important access, actions, changes, and decisions?
      O
      Ownership Does every important risk, control, exception, and decision have a named owner?
      K
      Key Control Reliability Do your most important controls work when they are tested?
      E
      Evidence Can you prove your controls and governance processes are actually working?
      R
      Response Are serious issues contained and resolved within the required time?
      I
      Improvement Are you preventing repeated failures, or simply recording them again?
      P
      Critical Exposure Penalty How much overdue and unaccepted risk currently sits above your approved risk level.
      80–100 Strong — supported by evidence

      Governance works and can be proven. An auditor, regulator, or customer could be shown the working, not just the intent.

      65–79 Working — important gaps remain

      The basics hold, but at least one factor is weak enough to undermine the others. Fix the weakest factor before adding new governance activity.

      50–64 Weak — corrective action required

      Governance exists on paper and partly in practice, but the organization cannot reliably prove control. Expect audit findings and avoidable surprises.

      Below 50 Serious unmanaged exposure

      Risk is being carried without visibility, ownership, or proof. This is an active exposure, not a documentation backlog.

      No score can exceed 64 while any factor sits at or below 40%, or 49 at or below 20% — because a weakest-link claim that averages weak links away is just marketing. When a cap applies, the calculator shows what the score would have been without it, and which factor caused it.

      Calculate your score → Measure it from your own data Read the method
      Start here · 2-minute story

      Governance vs. Risk, in one short voyage

      People mix these two up all the time. Here's the difference — no jargon. Click through the story.

      Chapter 1 · The voyage

      You just launched an AI into the world

      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.

      Chapter 2 · Governance

      Governance is the captain & the charts

      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.

      • Who is in command?
      • Where are we allowed to go?
      • What are the rules?
      • Who is accountable if something goes wrong?
      Chapter 3 · Risk Management

      Risk management is the radar & the lifeboats

      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.

      • What could go wrong?
      • How likely is it?
      • How bad would it be?
      • How do we prevent & survive it?
      Chapter 4 · Together

      Together, they reach port

      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.

      Assess your AI — free → Explore the Workbench
      AI Governance

      What is AI Governance?

      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.

      Proactive Compliance

      Adhering to regulatory frameworks before systems are audited or penalised.

      Continuous Bias Auditing

      Scanning input parameters to prevent algorithmic discrimination.

      Risk Mitigation

      Guarding against concept drift, data leakage, and adversarial vulnerability.

      AI Governance

      Navigating the Regulatory Frontier

      As global regulations evolve, understanding compliance requirements is key to avoiding liabilities and scaling trustworthy models. Click on the frameworks below to see details.

      EU AI Act (European Union) ▼

      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 since 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 — now law as Regulation (EU) 2026/1744, published in the Official Journal on 24 July 2026 and in force since 27 July 2026. Penalties reach €35M or 7% of global annual turnover (€15M or 3% for GPAI and transparency breaches).

      Risk Tiers & Rules:

      • Unacceptable Risk: Social scoring, manipulative/subliminal techniques, untargeted facial-image scraping and most real-time public biometric ID — strictly prohibited (Art. 5).
      • High Risk: Annex III uses such as recruiting, credit, medical and critical-infrastructure AI — demand a risk-management system, data governance, logging, human oversight, accuracy and conformity assessment (Arts. 9–15, 43).
      • Limited / Minimal Risk: Chatbots and general LLMs — transparency duties: disclose AI interaction and machine-readably mark synthetic content.
      NIST AI RMF (United States) ▼

      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.

      Four Core Pillars:

      • Govern: Establish safety culture and operational values.
      • Map: Identify AI system context and related risks.
      • Measure: Implement quantitative evaluation for safety, bias, and accuracy.
      • Manage: Deploy active risk mitigation mechanisms.
      ISO/IEC 42001 (International Standard) ▼

      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).

      Core Targets:

      • Aligns organizational AI policy with board objectives.
      • Tracks accountability throughout system life cycles.
      • Ensures standard risk-management loops for internal and outsourced models.
      FTC Enforcement Guidance (United States) ▼

      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.

      Key Focus Areas:

      • Deceptive Marketing: Over-promising AI capabilities or false claims of transparency ("AI washing").
      • Algorithmic Discrimination: Under Section 5, deploying models with unfair or biased outcomes can constitute an unfair practice.
      US State AI Laws (2026 wave) ▼

      Status: With no comprehensive US federal AI statute, the states are setting the pace. Key dates we track:

      • Colorado: the 2024 Colorado AI Act (SB 24-205) was repealed before taking effect and replaced by a narrower ADMT law, SB 26-189, effective 1 Jan 2027 — consumer notice, explanation and human review, AG-enforced.
      • Texas: the Responsible AI Governance Act (TRAIGA, HB 149) in force since 1 Jan 2026 — intent-based prohibitions, with a NIST AI RMF safe-harbour defence.
      • Illinois: HB 3773 (Human Rights Act amendment) since 1 Jan 2026 — bars discriminatory AI in employment, including zip-code proxies.
      • California: AB 2013 training-data transparency and SB 53 frontier-AI transparency since 1 Jan 2026; the CPPA's ADMT rules took effect Jan 2026 but their compliance obligations bite in 2027.
      • New York City: Local Law 144 — annual independent bias audits for automated hiring tools, enforced since July 2023.
      Interactive Tool

      Governance Applicability & Readiness Assessor

      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.

      Does the system do any of the following? (EU AI Act Art. 5 — prohibited)

      Fill in the factors above and select “Assess my system” to see your classification, obligations and readiness score.

      New Tool

      Readiness tells you what applies. The Effectiveness Score tells you how well it's actually working.

      A weighted governance model — coverage, visibility, ownership, control reliability, evidence, response and continuous improvement, less an exposure penalty — collapses into one defensible score. Free, runs in your browser, no sign-up.

      Calculate your Effectiveness Score →
      Free policy templates

      No AI Governance or AI Risk Management policy yet? We help you write them.

      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.

      Get the free policy templates → Want us to tailor them for you?
      AI Risk Management

      Governance Without Risk Management Is Structure Without Control

      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.

      Governance answers
      • Who owns AI decisions?
      • What policies do we follow?
      • What approvals are required?
      • What standards do we align to?
      Risk management asks
      • What can go wrong?
      • How likely is it, and how severe would the impact be?
      • Who is exposed?
      • How would we know if the risk is increasing?
      • What controls reduce the risk to an acceptable level?
      • When do we stop, escalate, or redesign?
      Governance sets direction.
      Risk management creates discipline.
      Governance defines accountability.
      Risk management defines controls.
      Governance asks who should decide.
      Risk management asks what could go wrong — and how we keep it within tolerance.

      A first-class discipline — not a footnote

      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.

      Identify

      Surface AI-specific risks across data, model, security, people and third parties.

      Assess & Quantify

      Score likelihood × impact, rank inherent vs. residual risk, visualise a heat map.

      Treat

      Apply controls — mitigate, transfer, avoid or knowingly accept — with named owners.

      Monitor

      Track key risk indicators, drift and incidents so residual risk stays in appetite.

      AI Risk Management

      Why Agentic AI Raises the Stakes

      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.

      Assistive AI

      Produces outputs

      A chatbot giving a wrong answer is a quality issue.

      Agentic AI

      Takes actions

      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 — your agents call our Agent Control Plane before they act, and it answers allow, review or deny in real time, with every decision sealed into a tamper-evident ledger. It's a checkpoint your systems consult, not a proxy that intercepts them, so the calling system has to honour the answer.

      Practical questions to ask of every agent

      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.

      Tick the checklist above to see your agentic exposure score.
      AI Risk Management

      The AI Risk Management Lifecycle

      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.

      01

      Identify the use case & business context

      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: MAP
      02

      Classify the risk

      Not 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: MAP
      03

      Map failure modes

      AI 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: MEASURE
      04

      Define risk appetite & thresholds

      Decide 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: GOVERN
      05

      Design controls before deployment

      Human-in-the-loop review, least-privilege access, approval gates, logging, monitoring, red teaming, testing, escalation paths, fallback procedures and clear ownership.

      NIST: MANAGE
      06

      Monitor continuously

      AI 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 · MANAGE
      07

      Connect to enterprise risk

      AI 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: GOVERN

      Pick the maturity of each stage in your organization today — your overall lifecycle maturity updates as you go.

      AI Risk Management

      AI Risk Taxonomy & Failure Modes

      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.

      Common AI failure modes

      “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.

      Tick the categories and failure modes above to see your risk-surface breadth.
      AI Risk Management

      Interactive AI Risk Assessment Matrix

      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.

      RareUnlikelyPossibleLikelyAlmost certain
      NegligibleMinorModerateMajorSevere
      Impact →
      Likelihood →
      Inherent risk 9 High
      Residual risk 5 Medium
      Recommended treatment Mitigate

      Reduce likelihood or impact with additional controls before deployment.

      Suggested controls for this category

        Educational risk-triage aid — not legal advice. For a defensible, audit-ready assessment, run your system through the Governance Workbench or book an engagement.

        Your AI Risk Register

        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.

        Combined Board Briefing

        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.

        AI Risk Management

        Frameworks & Standards We Operate By

        Our methodology is grounded in the recognised AI and enterprise risk standards — so your program is portable, auditable and defensible.

        NIST AI Risk Management Framework (AI RMF 1.0)▼

        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.

        Four core functions:

        • Govern: A culture of risk management across the organisation.
        • Map: Establish context and identify risks for each AI system.
        • Measure: Analyse, assess and track risks with quantitative & qualitative methods.
        • Manage: Prioritise and act on risks based on projected impact.
        ISO/IEC 23894:2023 — AI Risk Management▼

        Status: International standard giving AI-specific guidance on applying ISO 31000 risk management to artificial intelligence.

        Focus:

        • Integrates AI risk into existing enterprise risk processes.
        • Defines AI risk sources, lifecycle touchpoints and example controls.
        • Pairs directly with ISO/IEC 42001 (the AI management system standard).
        ISO 31000 — Enterprise Risk Management▼

        Status: The global parent standard for risk management of any kind.

        Principles we inherit:

        • Risk = effect of uncertainty on objectives.
        • Process: establish context → identify → analyse → evaluate → treat → monitor.
        • Risk treatment is proportionate to risk appetite and tolerance.
        EU AI Act — Risk Tiers & Art. 9 Risk System▼

        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).

        Risk tiers:

        • Unacceptable: Prohibited practices (social scoring, manipulation).
        • High: Annex III systems — full risk management, logging & oversight.
        • Limited / Minimal: Transparency duties; voluntary codes.
        OWASP Top 10 for LLM Applications▼

        Status: Industry security checklist for generative-AI applications.

        Headline risks:

        • Prompt injection, insecure output handling, training-data poisoning.
        • Model denial of service, supply-chain & plugin vulnerabilities.
        • Sensitive-information disclosure and excessive agency.
        MIT AI Risk Repository▼

        Status: A living, peer-reviewed database of 1,000+ documented AI risks.

        How we use it:

        • A checklist to make sure no plausible risk is missed during identification.
        • Causal & domain taxonomies to classify and compare risks consistently.

        Set an adoption status for each framework you follow — your coverage score updates as you go.

        AI Risk Management

        Risk Treatment, Appetite & Controls

        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.

        Treat (Mitigate)

        Apply controls to reduce likelihood or impact — testing, guardrails, human oversight, monitoring.

        Transfer

        Shift the risk via insurance, contractual indemnities, or vendor SLAs and warranties.

        Terminate (Avoid)

        Stop or redesign the use case when residual risk exceeds appetite and can’t be reduced.

        Tolerate (Accept)

        Knowingly accept low residual risk — documented, owned and signed off, with a review date.

        Define risk appetite & thresholds first

        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.

        Human-in-the-loop

        Mandatory review on consequential or irreversible actions.

        Least-privilege access

        Agents get the minimum data, tools and permissions they need.

        Approval gates

        Explicit sign-off before high-impact actions execute.

        Logging & auditability

        Every decision and action is recorded and reconstructable.

        Monitoring & alerting

        Real-time KRIs flag drift, breaches and anomalies.

        Red teaming & testing

        Adversarial testing against misuse, abuse and failure scenarios.

        Escalation paths

        Clear routes to stop, escalate or redesign when thresholds trip.

        Fallback & kill switches

        Safe defaults and the ability to halt an agent immediately.

        Key Risk Indicators & continuous monitoring

        Risk doesn’t stand still. We instrument the metrics that tell you when residual risk is drifting out of appetite.

        PSI < 0.2

        Data & Concept Drift

        Population stability & KS divergence between baseline and live data.

        ≥ 0.8

        Fairness Ratio

        4/5ths disparate-impact ratio across protected groups.

        Trend ▼

        Model Accuracy

        Live performance vs. validation baseline, with decay alerts.

        0 open

        Critical Incidents

        Open serious-incident count and mean time to remediate.

        < 1%

        Guardrail Breaches

        Jailbreak / prompt-injection / unsafe-output rate for LLM systems.

        100%

        Control Coverage

        Share of high-risk systems with controls implemented & tested.

        AI Risk Management

        An AI Risk Operating Model

        Accountability that is unambiguous, connected to enterprise risk, and built to mature — so you can scale AI safely, reliably and responsibly.

        1st Line

        Own & Manage

        Product, data-science & engineering teams who build and run AI — they own the risk day to day.

        2nd Line

        Oversee & Challenge

        AI risk, compliance & ethics functions setting policy, the risk appetite and the register.

        3rd Line

        Assure

        Internal audit & independent review providing assurance to the board and regulators.

        Connect AI risk to enterprise risk management

        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.

        Where organizations underestimate the challenge

        The common gap: governance forums without the risk discipline underneath them.

        Check any that describe your organization today.

        The next phase of AI maturity

        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:

        AI Risk Management

        AI Risk Management Services

        Engagements that take you from zero to a living, board-ready AI risk program.

        AI Risk Assessments

        System-by-system identification & scoring workshops producing a prioritised, inherent-vs-residual risk view.

        • Likelihood × impact heat map
        • EU AI Act tier classification

        Risk Register Build-Out

        A living risk register with owners, treatments, controls, due dates and residual-risk tracking.

        • Owned, dated, auditable entries
        • Risk-appetite thresholds

        Model Risk Management (MRM)

        Model validation, challenge and lifecycle controls per the interagency guidance (SR 26-2, successor to SR 11-7), adapted for ML & foundation models.

        • Independent model validation
        • Model inventory & tiering

        Third-Party & GenAI Risk

        Vendor & foundation-model due diligence, plus OWASP-LLM red-teaming for your generative-AI stack.

        • Vendor AI risk questionnaires
        • Prompt-injection red-teaming

        Continuous Risk Monitoring

        Stand up KRIs, drift & fairness dashboards and an incident-response playbook for live AI.

        • KRI thresholds & alerting
        • Incident-response runbooks

        Board & Risk-Committee Reporting

        Translate the register into the top-risk dashboards, appetite statements and trends your board needs.

        • Top-10 risk dashboards
        • Risk-appetite statements

        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.

        Ready to put a number on your AI risk?

        Run a real, private assessment in the Governance Workbench, or book a risk-strategy briefing with our team.

        Launch the Workbench → Book a Risk Briefing
        Autonomous Systems

        AutoGovern.io Agentic Hub

        Our autonomous AI Agents crawl global channels, catalog security incidents, generate real-time safety analyses, and answer visitor questions.

        Incident Crawler Agent

        ID: autogovern-crawler-01
        —

        Reads six public AI-news feeds every 30 minutes and records new incidents it finds.

        Current Task: —
        Incidents Tracked —

        Audit Analyst Agent

        ID: autogovern-analyst-02
        —

        Performs root-cause analysis on AI incident payloads and drafts mitigation playbooks and blog briefs.

        Current Task: —
        Analyses Published —

        Q&A Advocate Agent

        ID: autogovern-advisor-03
        —

        Engages with customers in real-time, resolving regulatory compliance queries and explaining AutoGovern features.

        Current Task: Waiting for a question
        Queries Resolved —
        AutoGovern-Agent-Operations.sh
        Crawler: Active Analyst: Active

        AutoGovern Governance Advisor

        AI Q&A Agent • Online

        Hello! I am the AutoGovern Governance Q&A agent. I autonomously monitor compliance issues and help teams design risk playbooks. Ask me any question about the EU AI Act, NIST AI RMF, bias mitigation, or continuous monitoring!

        AI Incident Database & Analysis

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        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.

        Synchronizing with global feeds…
        Written by our AI agents

        The autogovern.io Blog

        Every day our AI agents publish two fresh, technical posts — one on AI Governance, one on AI Risk Management. One reads the day’s real incidents.

        The other is an original thesis: an argument the agent forms itself from our live incident database, the regulation clock and anonymous platform telemetry, published only if it says something the blog has never said, and only with a dated prediction you can hold it to.

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        What We Do

        AI Governance Services For All Sizes

        From agile startups deploying lightweight APIs to global enterprises running hundreds of proprietary neural networks, our services scale with your needs.

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        Interactive Governance Suite

        Evaluate your model's regulatory risk profile in real-time or simulate live model metric monitoring. Choose an application tab below to start.

        Standard Enforced

        Model Risk Classification

        Minimal Risk
        Estimated Obligations under EU AI Act & NIST:
        Live — Agent Control Plane
        Actions governed
        0 sealed
        Blocked / held by policy
        0 0%
        PII values masked in the ledger
        0 never stored raw
        Action risk — recent activity
        Action risk score
        Deny threshold

        Recent interventions 0

        Ledger integrity & controls

        Fleet kill switch Halt every non-read agent action
        Chain integrity —
        Head hash —
        Free Download · No Signup

        Aegis — Free AI Security Scanner Free Air-gapped / CLI-only

        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.

        Read-only — never changes your system Runs fully offline / air-gapped Zero dependencies · ~8 MB CIS · MITRE ATT&CK mapped

        macOSNo terminal

        FileVault, firewall, Gatekeeper, SIP, remote login, exposed ports.

        Download for Mac Universal app · Apple-notarized · double-click to scan
        Prefer the command-line binary? Apple Silicon (arm64) Intel (amd64)

        Windows

        Firewall profiles, Defender, SMBv1, RDP NLA, UAC, Guest account, BitLocker.

        x64 amd64 ↓ ARM64 arm64 ↓

        Linux

        SSH hardening, shadow perms, sudo, firewall, patch backlog, listeners.

        x86-64 amd64 ↓ ARM64 arm64 ↓

        ▸ Run it in 10 seconds

        # 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.sarif

        Tip: run with sudo / an elevated shell for full coverage of protected files. Cloud collectors (AWS · Azure · GCP) are coming next.

        SHA-256 verified · view checksums Signed report available in the Governance Workbench Already run a Trivy/Grype scan? Triage it with Pulse ↓
        Free Download · No Signup

        Pulse — Free Exploit-Priority CVE Triage Free Air-gapped / CLI-only

        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.

        Read-only — never changes your system Works offline on a cached snapshot Zero dependencies · ~6 MB CISA KEV · EPSS mapped
        Command-line tool — not a double-click app. Unlike Aegis, Pulse has no macOS/Windows GUI installer. Downloading it gives you a plain executable; double-clicking it in Finder/Explorer will just prompt "no application can open this file" — that's expected. Open a terminal and run the commands below instead.
        The only network call Pulse ever makes: running 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.

        macOSTerminal

        Apple Silicon and Intel builds — scan a path or triage an existing SCA report.

        Apple Silicon arm64 ↓ Intel amd64 ↓

        WindowsTerminal

        Single x64 build — run from PowerShell or a CI runner.

        x64 amd64 ↓

        LinuxTerminal

        Scan a container image root, a repo checkout, or the whole host.

        x86-64 amd64 ↓ ARM64 arm64 ↓

        ▸ Two ways to use it

        # 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 exploitation

        Findings 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.

        SHA-256 verified · view checksums Scanning host security posture instead? Try Aegis ↑
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