AI Risk Assessment Guide

Your organisation is almost certainly using AI — the question is whether you know which AI tools, how they're being used, and what risks they introduce. A structured AI risk assessment answers all three.

Unlike traditional IT risk assessments, AI introduces unique challenges: model opacity, data dependency, emergent behaviours, and rapidly evolving regulatory requirements. Standard risk frameworks need adaptation to address these properties effectively.

This guide walks you through a complete AI risk assessment methodology — from scoping and inventory through risk scoring, mitigation planning, and ongoing monitoring. Whether you're assessing your first AI tool or building an enterprise-wide programme, you'll leave with a repeatable, audit-ready process.

Why AI Risk Assessments Are Different

Traditional IT risk assessments focus on confidentiality, integrity, and availability of systems and data. AI risk assessments must go further, covering dimensions that don't exist in conventional software:

• Model risk: AI outputs are probabilistic, not deterministic. The same input can produce different outputs, making testing and validation fundamentally different.
• Data risk: AI models are shaped by their training data. Biased, incomplete, or poisoned data creates downstream risks that compound over time.
• Supply chain risk: Most organisations consume AI through third-party APIs. You inherit the provider's security posture, data practices, and model governance.
• Regulatory risk: The EU AI Act, NIST AI RMF, ISO 42001, and sector-specific regulations are creating new compliance obligations that are still being interpreted.
• Ethical and reputational risk: AI systems can produce biased, offensive, or harmful outputs that create legal liability and brand damage.

For key terminology, refer to the Aona AI Glossary.

Step 1: Define Scope and Objectives

Before assessing anything, define what you're assessing and why. Scope decisions dramatically affect effort and outcomes.

Scoping Questions

• Are you assessing a single AI tool, a department's AI usage, or the entire organisation?
• Is this driven by a regulatory requirement, an incident, or proactive governance?
• Which risk framework(s) will you align to — NIST AI RMF, ISO 42001, EU AI Act, or internal?
• Who are the key stakeholders (security, legal, compliance, business owners, data teams)?
• What's the timeline and reporting cadence?

Pro tip: Start narrow. Assess your highest-risk AI system first — typically the one with the most sensitive data access or the widest user base. Use this to refine your methodology before scaling.

Step 2: Build Your AI Inventory

You can't assess what you don't know exists. Building a comprehensive AI inventory is the foundation of any risk assessment.

For each AI system, capture:

1. System name and vendor
2. Business purpose and use case
3. Data inputs — what data does it access, process, or store?
4. Data outputs — what does it produce and who consumes it?
5. Integration points — APIs, data pipelines, other systems connected
6. User base — who uses it and how many?
7. Deployment model — SaaS, API, on-premise, embedded?
8. Business owner and technical owner

Don't forget shadow AI — tools adopted by teams without IT involvement. Survey business units, check expense reports for AI subscriptions, and review network logs for AI service domains.

Step 3: Identify and Categorise Risks

For each AI system in your inventory, systematically identify risks across these categories:

• Data Security: Data leakage, unauthorised access, data retention, cross-border transfers
• Privacy: PII processing, consent management, data subject rights, privacy impact
• Compliance: Regulatory requirements, audit readiness, documentation completeness
• Operational: Availability, vendor lock-in, model degradation, integration failures
• Ethical: Bias, fairness, transparency, explainability, human oversight
• Reputational: Public perception, brand damage, stakeholder trust

Step 4: Score and Prioritise Risks

Use a consistent scoring methodology to prioritise risks. A simple but effective approach uses a 5×5 matrix:

Likelihood (1-5): 1 = Rare, 2 = Unlikely, 3 = Possible, 4 = Likely, 5 = Almost certain

Impact (1-5): 1 = Negligible, 2 = Minor, 3 = Moderate, 4 = Major, 5 = Catastrophic

Risk Score = Likelihood × Impact. Scores of 15-25 are critical, 8-14 are high, 4-7 are medium, and 1-3 are low.

For a ready-to-use risk scoring template with automated calculations, download our AI risk assessment templates.

Step 5: Develop Mitigation Plans

For each high and critical risk, define a mitigation strategy using the standard risk treatment options:

• Mitigate: Implement controls to reduce likelihood or impact (e.g., access controls, monitoring, data classification)
• Transfer: Shift risk to a third party (e.g., insurance, contractual SLAs with AI vendors)
• Accept: Formally acknowledge and document the risk with leadership sign-off
• Avoid: Stop using the AI system or remove the risky capability entirely

Each mitigation plan should include: the specific control or action, the owner responsible, the target completion date, and how effectiveness will be measured.

Step 6: Establish Ongoing Monitoring

AI risk assessment is not a one-time exercise. The AI landscape changes rapidly — new models, new regulations, new attack vectors, and new use cases emerge constantly. Build a continuous monitoring programme:

1. Quarterly reviews of the AI inventory and risk register
2. Automated alerts for new AI tool adoption (via network monitoring, SSO logs, expense tracking)
3. Regulatory watch for new AI-specific regulations and guidance in your jurisdiction
4. Incident tracking for AI-related security events and near-misses
5. Annual full reassessment aligned with your organisation's risk management cycle

Aligning with Frameworks: NIST AI RMF and ISO 42001

Two frameworks are emerging as the gold standard for AI risk management:

NIST AI Risk Management Framework (AI RMF) provides a voluntary framework organised around four functions: Govern, Map, Measure, and Manage. It's flexible, non-prescriptive, and widely adopted in the US.

ISO/IEC 42001 is the international standard for AI management systems. It provides certifiable requirements for establishing, implementing, and continually improving AI governance. It's particularly valuable for organisations operating across jurisdictions.

For a detailed comparison of these frameworks, see our framework comparison guides.

How Aona AI Streamlines AI Risk Assessments

Running AI risk assessments manually — with spreadsheets, email threads, and quarterly reviews — doesn't scale. As AI adoption accelerates, security teams need tooling that keeps pace.

Aona AI's platform automates the heavy lifting of AI risk management:

• Automatic AI discovery — detect new AI tools as they're adopted, including shadow AI
• Pre-built risk assessment templates aligned with NIST AI RMF, ISO 42001, and the EU AI Act
• Automated risk scoring with continuous monitoring and alerting
• Audit-ready reporting that satisfies regulators and board-level governance requirements

Get started with our free AI risk assessment templates, or explore our industry guides for sector-specific risk assessment guidance.