Govern & De-Risk AI at the Speed of Innovation
Your end-to-end partner for trustworthy AI. woose.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.
We never see your data — it never leaves your browser. Run real fairness, drift & risk analysis on your own AI system, with an AI agent guiding every fix. No sign-up.
100%
Private — browser-only
16+
Jurisdictions covered
ISO 42001
Framework aligned
Loading the regulatory horizon…
You Shouldn't Need a Sales Call to Find Out If You Have a Problem
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.
Book a demo to find out
"Free" usually means a glossary, a checklist, or a gated report. The actual assessment sits behind a sales conversation.
Run it right now
The Governance Workbench is the actual product — 17 real tools, free and private, running the moment you open it. No call required.
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.
What is AI Governance?
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.
EU AI Act (European Union) ▼
Status: Legally Enacted. The world's first comprehensive horizontal law governing AI.
Risk Tiers & Rules:
- Unacceptable Risk: Cognitive manipulation, social scoring, biometric classification. (Strictly prohibited).
- High Risk: Recruiting algorithms, medical diagnostic systems, critical infrastructure (Demands pre-market conformity, human oversight, logging).
- Limited/Minimal Risk: Chatbots, general LLMs (Requires basic transparency labeling e.g. "AI generated").
NIST AI RMF (United States) ▼
Status: Voluntary standard widely adopted by US Government and tech sectors.
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 in late 2023. Certification standard for an AI Management System (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: Active enforcement under consumer protection and anti-discrimination statutes.
Key Focus Areas:
- Deceptive Marketing: Over-promising AI capabilities or false claims of transparency.
- Algorithmic Discrimination: Under Section 5 (unfair practices), deploying models with demographic bias is subject to severe structural penalties.
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.
Fill in the factors above and select “Assess my system” to see your classification, obligations and readiness score.
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.
- Who owns AI decisions?
- What policies do we follow?
- What approvals are required?
- What standards do we align to?
- 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?
Risk management creates discipline.
Risk management defines controls.
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. woose.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.
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.
Produces outputs
A chatbot giving a wrong answer is a quality issue.
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 — our Agent Control Plane governs the action path itself, in real time, with every decision sealed into a tamper-evident ledger.
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.
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.
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: MAPClassify 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: MAPMap 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: MEASUREDefine 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: GOVERNDesign 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: MANAGEMonitor 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 · MANAGEConnect 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: GOVERNPick the maturity of each stage in your organization today — your overall lifecycle maturity updates as you go.
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.
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.
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.
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.
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 (2023), with a 2024 Generative AI Profile. 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. Article 9 mandates a continuous risk-management system for high-risk AI.
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.
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.
Data & Concept Drift
Population stability & KS divergence between baseline and live data.
Fairness Ratio
4/5ths disparate-impact ratio across protected groups.
Model Accuracy
Live performance vs. validation baseline, with decay alerts.
Critical Incidents
Open serious-incident count and mean time to remediate.
Guardrail Breaches
Jailbreak / prompt-injection / unsafe-output rate for LLM systems.
Control Coverage
Share of high-risk systems with controls implemented & tested.
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.
Own & Manage
Product, data-science & engineering teams who build and run AI — they own the risk day to day.
Oversee & Challenge
AI risk, compliance & ethics functions setting policy, the risk appetite and the register.
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 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 woose.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.
Woose.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: woose-crawler-01Scrapes global news feeds, arXiv disclosures, and code repos for emerging AI failures and vulnerability payloads.
Audit Analyst Agent
ID: woose-analyst-02Performs root-cause analysis on AI incident payloads and drafts mitigation playbooks and blog briefs.
Q&A Advocate Agent
ID: woose-advisor-03Engages with customers in real-time, resolving regulatory compliance queries and explaining Woose features.
Woose Governance Advisor
AI Q&A Agent • Online
AI Incident Database & Analysis
Live feedA 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.
The woose.io Blog
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.
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.
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.
Model Risk Classification
Minimal RiskEstimated Obligations under EU AI Act & NIST:
Aegis — Free AI Security Scanner
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.
macOSNo terminal
FileVault, firewall, Gatekeeper, SIP, remote login, exposed ports.
Windows
Firewall profiles, Defender, SMBv1, RDP NLA, UAC, Guest account, BitLocker.
Linux
SSH hardening, shadow perms, sudo, firewall, patch backlog, listeners.
▸ 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.sarifTip: run with sudo / an elevated shell for full coverage of protected files. Cloud collectors (AWS · Azure · GCP) are coming next.
Partner with woose.io
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.
Email Advisory
governance@woose.io
Free Workbench
Run your assessment now — no sign-up needed