Assess AI risks across business and regulation
Continuously assess, qualify, and prioritize AI risks across regulatory and business dimensions.
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AI adoption is accelerating—so are the risks
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Evolving frameworks like the EU AI Act and global regulations are creating immediate and complex compliance obligations.
Regulatory exposure
is intensifying
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Organizations struggle to accurately qualify AI risk levels and determine the right course of action.
Identifying risk is no longer straightforward
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Operational, financial, reputational, and strategic risks must be assessed alongside regulatory requirements to get a complete picture.
Risk goes beyond compliance
Built-in granular risk qualification
Naaia delivers granular regulatory risk qualification across global AI frameworks, including the EU AI Act, Chinese regulations, U.S. state-level laws, and beyond.
The platform determines your operator role and risk level at the asset level, enabling precise classification and automatically generating tailored compliance action plans.
Operationalize your own internal frameworks
Naaia integrates your internal risk methodologies directly into the platform.
This allows you to extend beyond regulatory requirements and perform fully aligned, organization-specific risk assessment, ensuring consistency with your governance, risk, and compliance standards.
Manage universal risks
Naaia natively provides a risk matrix built on the risk taxonomies defined by leading institutions such as MIT and OWASP.
It structures AI risks into comprehensive families and enables you to assess them through impact and likelihood, giving you a rigorous, decision-grade foundation to prioritize and act.
With Naaia
Qualify risk with confidence
Accurately determine regulatory risk levels and operator status with audit-ready precision.
Integrate your own risk frameworks
Embed your internal methodologies to achieve a unified and consistent view of risk.
Continuously monitor risk evolution
Track risk over time with dynamic updates aligned to regulatory changes and operational reality.
Trusted by those who build with AI
Naaia enabled us to move toward a more value-driven approach to AI governance. Beyond compliance, the platform helps us prioritize AI initiatives based on business impact, risk, and strategic value.
Colas naturally selected Naaia for its intuitive user experience, as well as its educational and thought-leadership approach to simplifying complex AI topics. A truly collaborative framework was established throughout the project, driving stronger team engagement, seamless adoption, and operational efficiency.
We chose Naaia for its clear framework to inventory and govern our AI initiatives. We particularly appreciate the combination of its user-friendly tools, which make compliance more accessible, and the supportive guidance from their team.
Naaia helped us operationalize AI Act readiness across our regional authority’s services with a clear and trusted governance framework. The platform improved collaboration between departments and gave us the visibility needed to scale AI responsibly.
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Frequently asked questions
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Why is AI risk assessment now a legal requirement under the EU AI Act?
The EU AI Act establishes mandatory risk management as a core obligation for providers and deployers of high-risk AI systems (Article 9). Organizations must implement a continuous risk management system that identifies, analyzes, and mitigates risks throughout the AI system lifecycle — from design to post-deployment. This is not a one-time assessment but an ongoing process that must be documented and updated as the system evolves or its context of use changes.
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What methodology should organizations use to assess and classify AI system risk levels?
A structured AI risk assessment methodology should follow four steps:
(1) Scope identification — determine whether the system falls under EU AI Act prohibited uses, high-risk categories (Annex I and Annex III), limited transparency obligations, or minimal risk.
(2) Risk analysis — evaluate the potential harm to fundamental rights, safety, or health of affected individuals.
(3) Mitigation design — implement technical and procedural controls proportionate to the identified risks.
(4) Documentation — record the assessment, controls, and rationale in a format suitable for regulatory inspection.
ISO/IEC 42001 Annex A and the NIST AI RMF provide complementary methodological guidance.
Naaia combines global regulatory frameworks with organization-specific methodologies to assess AI risks at the asset level. The platform evaluates risk exposure across multiple dimensions and helps organizations classify systems, prioritize actions and maintain consistent governance practices. -
How can organizations implement continuous AI risk monitoring across all deployed systems?
Continuous AI risk monitoring requires four operational capabilities:
(1) a live inventory of all deployed systems with their approved risk profiles;
(2) automated anomaly detection to flag deviations in model behavior, output distribution, or usage patterns;
(3) a defined escalation process for incidents that exceed risk thresholds; and
(4) periodic formal re-assessment cycles — at least annually for high-risk systems, more frequently for rapidly evolving models.
Monitoring should cover both technical dimensions (model drift, data quality) and contextual dimensions (changes in how the system is used or who it affects). -
How can organizations operationalize AI risk management?
Operationalizing AI risk management means embedding governance into the everyday workflows of the teams that build and use AI — not treating it as a compliance exercise done once a year. This requires: assigning clear ownership for each AI system, integrating risk assessment into the AI development and procurement lifecycle, establishing a risk register with defined review triggers, training frontline users on their human oversight responsibilities, and building an escalation path for AI-related incidents. Organizations that operationalize risk management reduce both their regulatory exposure and the likelihood of costly AI failures.