Control A.5.2 –

AI System Impact Assessment Process

While Control A.5.1 requires organizations to assess the impacts of AI systems, Control A.5.2 goes a step further—it requires organizations to establish a repeatable, structured process for carrying out those assessments.

In other words, it’s not enough to assess impacts once. Organizations must define, implement, and maintain a formal impact assessment process that ensures consistency, accountability, and traceability across all AI systems.

🔑 What This Control Means

Organizations should:

Define a methodology for impact assessments, including scope, frequency, and depth.
Ensure the process covers ethical, social, legal, technical, and environmental dimensions of AI impacts.
Assign responsibilities for who conducts, reviews, and approves assessments.
Establish documentation and evidence requirements to demonstrate compliance.
Integrate the process with risk management and system development lifecycles.
Require reassessments whenever the AI system undergoes significant changes, new risks emerge, or regulations evolve.

✅ Why It Matters

Consistency – A standardized process ensures assessments are carried out uniformly across projects.
Accountability – Clear roles and approvals prevent gaps or oversight in impact evaluation.
Auditability – Documented assessments provide evidence during internal and external audits.
Proactive Governance – Helps organizations identify issues before they escalate into legal, ethical, or reputational risks.
Regulatory Alignment – Many AI regulations (e.g., EU AI Act) will require formal assessment processes, not ad hoc evaluations.

📌 Implementation Tip

Develop a standard AI Impact Assessment (AIIA) template with structured sections (purpose, stakeholders, risks, mitigations).
Use a risk-rating system to prioritize mitigation efforts.
Integrate assessments with Data Protection Impact Assessments (DPIAs) where personal data is involved.
Create review cycles—e.g., initial assessment, periodic reassessment, and post-incident reassessment.
Provide training for staff to apply the process consistently.

By embedding a formal impact assessment process into the AI lifecycle, organizations move from one-time risk evaluation to continuous, structured AI governance, ensuring both compliance and trustworthiness.

In tomorrow’s article by Kimova.AI, we’ll explore Annex A Control A.5.3 – Documentation of AI System Impact Assessments, where we’ll explore how organizations can maintain clear, structured, and accessible records of impact assessments to support accountability, transparency, and compliance in AI operations.

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