Cyberrisk governance for AI

Cyberrisk governance for AI refers to the practices and frameworks used to identify, manage, and mitigate cybersecurity risks in artificial intelligence systems. These include risks tied to data poisoning, model inversion, unauthorized access, and other vulnerabilities that arise from AI-specific threats or weaknesses.

This topic matters because AI systems are becoming part of critical infrastructure, decision-making processes, and personal data handling. A single ungoverned model can create serious security gaps.

For compliance, risk, and governance teams, cyberrisk governance is essential to meet global standards like ISO/IEC 42001 and to avoid data breaches, regulatory fines, or reputational harm.

Why AI systems face unique cyberrisks

AI introduces new attack surfaces that do not exist in traditional software. Models can be tricked into producing incorrect outputs through adversarial inputs, or attackers may try to reverse-engineer model logic to access sensitive data. Unlike static code, AI systems evolve with data, which can be manipulated.

Additionally, AI often relies on large external datasets, third-party APIs, or pre-trained models, any of which can introduce hidden vulnerabilities. This makes cyberrisk governance both broader and more complex than conventional IT security.

Common cyberthreats targeting AI

There are several recurring patterns in attacks against AI systems. Knowing them is the first step in building defenses.

• Data poisoning: Injecting incorrect or malicious data into training pipelines to manipulate model behavior.

• Model theft: Reconstructing proprietary models through repeated querying and output analysis.

• Inference attacks: Using outputs to guess sensitive attributes about individuals in the training data.

• Adversarial examples: Crafting subtle input changes that confuse or mislead the model.

• Unauthorized access: Gaining control of models or pipelines due to weak authentication or poor access control.

These threats affect not only security but also fairness, accuracy, and trust in AI results.

Real-world examples

A major facial recognition provider was targeted by adversarial input attacks that fooled its system into misidentifying individuals. The flaw went unnoticed until third-party researchers published their findings.

In another case, a financial prediction model was poisoned through an external data feed that had been subtly manipulated by attackers. This resulted in skewed risk scores for loans and credit decisions.

Best practices for AI cyberrisk governance

Strong governance starts with understanding AI’s unique threat model and setting up layered protections. Cybersecurity teams must collaborate closely with data science and legal functions to build AI-specific controls.

Effective practices include:

• Conduct threat modeling: Use AI-focused security assessments like those recommended by NIST AI RMF.

• Implement access control: Restrict who can update, run, or export models. Use logging to track usage.

• Test for adversarial risk: Use tools like CleverHans or Foolbox to simulate attacks.

• Validate training data sources: Verify and audit external data to reduce poisoning risks.

• Encrypt and isolate models: Store models securely and isolate sensitive training pipelines.

Cyberrisk management should align with broader frameworks like ISO/IEC 27001 and ISO/IEC 42001 for AI-specific controls.

FAQ

How is cyberrisk for AI different from regular IT risk?

AI risks include model-specific attacks like adversarial examples and inference attacks, which do not affect traditional software in the same way. AI also brings new data flows and external dependencies that change the attack surface.

Who is responsible for cyberrisk governance in AI projects?

It should be shared. AI teams must understand threat vectors, while cybersecurity teams must adapt policies. Risk and compliance teams should oversee the integration into broader governance processes.

Are there AI-specific cybersecurity frameworks?

Yes. The NIST AI RMF, ISO/IEC 42001, and MITRE ATLAS provide structured guidance for identifying and managing AI-related threats.

What happens if AI cybersecurity risks are ignored?

Vulnerabilities may lead to privacy violations, manipulated outputs, service disruptions, or theft of intellectual property. Legal consequences could also apply, especially under regulations like the EU AI Act.

How does AI cyber risk differ from traditional cyber risk?

AI introduces unique attack surfaces: adversarial inputs, model extraction, data poisoning, and prompt injection. AI security requires understanding of ML vulnerabilities alongside traditional security. AI systems may have different trust boundaries and data flows than traditional software. Security teams need AI literacy; AI teams need security awareness.

What governance structures address AI cyber risk?

Integrate AI cyber risk into existing cybersecurity governance. Extend threat modeling to cover AI-specific attacks. Include AI security in security policies and standards. Conduct AI-focused security assessments. Ensure incident response covers AI-specific scenarios. Board-level cyber risk reporting should include material AI risks.

How do you assess AI cyber risk?

Assessment includes: threat modeling for AI-specific attacks, vulnerability scanning of ML infrastructure, review of access controls on models and data, supply chain risk assessment for pre-trained models and libraries, penetration testing including adversarial attacks, and incident history analysis. Regular reassessment keeps pace with evolving threats.

Summary

AI systems introduce cybersecurity risks that are complex, evolving, and high-stakes. Cyberrisk governance for AI means treating these systems as unique security assets that require dedicated testing, protection, and policy.

As AI becomes more embedded in critical operations, strong cyberrisk practices are no longer optional—they are fundamental to building secure and trustworthy AI.