Learning Objectives — AI Risk Registers & Control Catalogs

By the end of this lesson, learners will be able to:

• Define the purpose of an AI risk register.
• Explain how AI risk registers support governance and oversight.
• Identify common categories within an AI risk taxonomy.
• Describe methods for scoring AI risks based on likelihood and impact.
• Understand how risk ratings support prioritization decisions.
• Explain the role of control catalogs in risk management.
• Map AI risks to security and compliance frameworks.
• Describe risk treatment strategies used within AI programs.
• Understand the importance of lifecycle reviews and continuous updates.
• Apply risk register and control catalog concepts to certification exam scenarios.

Key Concepts — AI Risk Registers & Control Catalogs

• AI Risk Register
• Risk Management
• Risk Ownership
• Risk Taxonomy
• Data Risk
• Model Risk
• Operational Risk
• Ethical Risk
• Compliance Risk
• Risk Scoring
• Likelihood
• Impact
• Risk Rating
• Residual Risk
• Risk Prioritization
• Risk Treatment
• Risk Acceptance
• Risk Mitigation
• Control Catalog
• NIST AI RMF
• NIST SP 800-53
• ISO/IEC 42001
• EU AI Act
• Continuous Monitoring
• Governance Oversight

Transcript — AI Risk Registers & Control Catalogs

Welcome to Lesson 1.3: AI Risk Registers and Control Catalogs.

In our previous lesson, we examined how organizations build AI security programs through governance structures, policies, accountability frameworks, and continuous improvement processes.

Once an organization establishes an AI security program, the next challenge becomes managing risk in a structured and repeatable manner.

Organizations need a way to identify risks, document them, assign ownership, prioritize remediation efforts, and track mitigation activities over time.

This is where AI risk registers and control catalogs become essential.

Together, these tools provide the foundation for organized AI risk management.

They help transform uncertainty into actionable information that supports governance, security, compliance, and executive decision-making.

Let’s begin by examining the purpose of an AI risk register.

A risk register is a centralized repository used to document and manage risks throughout their lifecycle.

In traditional enterprise risk management programs, risk registers help organizations track cybersecurity risks, operational risks, financial risks, and compliance risks.

AI risk registers serve a similar purpose but focus specifically on risks associated with artificial intelligence systems.

An AI risk register captures information about identified risks, including descriptions, categories, ownership, likelihood, impact, mitigation activities, and current status.

Rather than storing risk information across emails, spreadsheets, and isolated reports, organizations maintain a structured record that provides visibility into the overall risk landscape.

This visibility is critical because AI systems often introduce risks that span multiple business functions.

A single model may involve data governance concerns, privacy implications, security vulnerabilities, operational dependencies, and regulatory obligations.

The risk register provides a common framework for managing these interconnected issues.

One of the most important functions of a risk register is accountability.

Every documented risk should have an assigned owner.

The owner is responsible for monitoring the risk, coordinating mitigation activities, and reporting status updates.

Without ownership, risks often remain unresolved because responsibility becomes unclear.

Assigning ownership ensures accountability and creates a clear point of contact for governance and oversight activities.

Another important element of an AI risk register is risk categorization.

Organizations often use a risk taxonomy to group similar risks into standardized categories.

A taxonomy creates consistency and improves communication across teams.

Although organizations may customize categories based on their specific needs, most AI risk taxonomies include several common areas.

The first category is data risk.

Data risks include concerns related to data quality, bias, privacy, integrity, lineage, and availability.

Examples include biased training datasets, incomplete records, unauthorized access to sensitive information, and data poisoning attacks.

The second category is model risk.

Model risks focus on the behavior and performance of AI models.

Examples include model drift, overfitting, adversarial manipulation, model theft, and inaccurate predictions.

The third category is operational risk.

Operational risks involve the systems, processes, infrastructure, and personnel that support AI operations.

Examples include inadequate monitoring, configuration errors, process failures, or insufficient governance controls.

The fourth category is ethical risk.

Ethical risks include fairness concerns, transparency challenges, explainability limitations, and accountability issues.

The fifth category is compliance risk.

Compliance risks involve regulatory obligations, industry standards, contractual requirements, and governance expectations.

By organizing risks into categories, organizations gain a clearer understanding of where exposure exists and where additional controls may be needed.

Once risks are identified and categorized, organizations must evaluate their significance.

This process is commonly known as risk scoring.

Risk scoring provides a structured approach for assessing and comparing risks.

Although scoring methodologies vary, most approaches evaluate two primary factors:

Likelihood and impact.

Likelihood refers to the probability that a risk event will occur.

Impact refers to the potential consequences if that event occurs.

For example, a model drift issue affecting a non-critical internal application may have moderate likelihood but low impact.

Conversely, bias within a credit approval model may have moderate likelihood but extremely high impact due to legal, financial, and reputational consequences.

Organizations often combine likelihood and impact scores to produce an overall risk rating.

Common ratings include:

Low.

Medium.

High.

And critical.

These ratings help decision-makers understand which risks require immediate attention and which can be addressed through longer-term planning.

Risk scoring supports prioritization.

Organizations rarely have unlimited resources.

As a result, they must decide where to focus remediation efforts.

Prioritization ensures that the most significant risks receive appropriate attention.

However, identifying and scoring risks is only part of the process.

Organizations must also determine how risks will be managed.

This process is known as risk treatment.

Several common risk treatment strategies exist.

The first is risk mitigation.

Mitigation involves implementing controls that reduce likelihood, impact, or both.

Examples include encryption, access controls, monitoring systems, security testing, model validation procedures, and governance reviews.

The second strategy is risk acceptance.

Some risks may fall within the organization’s risk appetite.

In these cases, leadership may formally accept the risk while continuing to monitor it.

The third strategy is risk transfer.

Organizations may transfer certain risks through insurance, contractual agreements, or third-party service arrangements.

The fourth strategy is risk avoidance.

Avoidance involves eliminating the activity creating the risk.

For example, an organization may decide not to deploy a specific AI system if risks exceed acceptable thresholds.

Risk treatment decisions should be documented within the risk register to maintain transparency and accountability.

Now let’s discuss control catalogs.

A control catalog is a structured collection of security, governance, compliance, and operational controls.

Control catalogs help organizations identify safeguards that address specific risks.

Rather than creating controls from scratch, organizations often leverage established frameworks and standards.

Several widely recognized control frameworks exist.

The NIST AI Risk Management Framework provides guidance for managing AI-related risks throughout the AI lifecycle.

NIST Special Publication 800-53 contains a comprehensive catalog of security and privacy controls.

ISO/IEC 42001 provides requirements for AI management systems.

The EU AI Act introduces governance and compliance expectations for AI systems operating within regulated environments.

Control catalogs create an important connection between identified risks and mitigation activities.

For example, a risk involving unauthorized access to model artifacts may map to controls involving authentication, authorization, encryption, and monitoring.

A data privacy risk may map to controls involving consent management, access restrictions, and retention policies.

By mapping risks to controls, organizations create traceability between risk management activities and security implementations.

This traceability supports audits, compliance reviews, and governance reporting.

One significant advantage of control catalogs is consistency.

Different teams across the organization can apply the same controls to similar risks.

This reduces duplication, improves governance, and simplifies oversight activities.

Control mapping also supports what is sometimes called a “one control, many frameworks” approach.

A single security control may simultaneously support NIST requirements, ISO standards, regulatory obligations, and internal governance expectations.

This improves efficiency while reducing compliance complexity.

Risk management does not end after risks are documented and controls are assigned.

AI systems evolve continuously.

Models are retrained.

Datasets change.

Business requirements shift.

Threat actors develop new techniques.

Regulatory expectations continue to expand.

Because of this, risk registers must remain living documents.

Organizations should review risk registers regularly.

Many organizations perform quarterly reviews, while others conduct reviews after significant model releases, architecture changes, incidents, or regulatory updates.

These reviews help ensure that risk information remains current and accurate.

Lifecycle reviews also evaluate control effectiveness.

A control that was effective six months ago may no longer provide sufficient protection as technologies and threats evolve.

Continuous monitoring plays a critical role in maintaining risk visibility.

Monitoring activities may include model performance assessments, security testing, audit reviews, compliance evaluations, and incident analysis.

Insights from monitoring activities should feed back into the risk register, creating a cycle of continuous improvement.

Let’s consider a practical example.

Imagine a healthcare organization deploying an AI model that assists physicians with patient diagnosis recommendations.

Several risks may be identified.

Data risks involving patient privacy.

Model risks involving inaccurate predictions.

Operational risks involving system availability.

Compliance risks involving healthcare regulations.

Each risk is documented within the risk register.

Likelihood and impact are assessed.

Ownership is assigned.

Relevant controls are mapped from security and governance frameworks.

Monitoring activities track effectiveness over time.

This structured approach enables leadership to understand exposure, prioritize resources, and maintain accountability.

Without the risk register and associated control mappings, managing these risks would be significantly more difficult.

For certification exams, remember several key concepts.

AI risk registers serve as centralized repositories for documenting and managing AI-related risks.

Risk taxonomies help categorize risks consistently.

Likelihood and impact drive risk scoring.

Risk treatment strategies include mitigation, acceptance, transfer, and avoidance.

Control catalogs provide structured collections of safeguards.

Frameworks such as NIST AI RMF, NIST SP 800-53, ISO/IEC 42001, and the EU AI Act commonly support control mapping activities.

And finally, risk management is continuous.

Risk registers should remain living documents that evolve alongside AI systems.

To summarize, AI risk registers and control catalogs provide the operational foundation for structured AI risk management.

They improve visibility, accountability, prioritization, governance, and compliance.

By connecting identified risks with appropriate controls and maintaining continuous oversight, organizations can manage AI risks more effectively while supporting trustworthy and resilient AI operations.

In the next lesson, we’ll explore how AI risks integrate into Enterprise Governance, Risk, and Compliance programs, connecting AI oversight directly with broader organizational risk management practices.