Artificial intelligence has become a permanent part of SOC and CTI teams, but as automation grows, so does the need to control technical, legal, and ethical risk. This is the role of AI governance – the set of policies, roles, and processes that guide AI systems through their entire lifecycle so they are safe, reliable, and compliant. In practice, this is anchored in recognized frameworks and standards such as the NIST AI Risk Management Framework and ISO/IEC 42001 – the first global AI management system standard.

What AI Governance means in CTI

In threat intelligence, governance ties together three layers: data policy and compliance, model risk management, and operational oversight of automation. NIST AI RMF stresses identifying and reducing risks to people and organizations and embedding the properties of “trustworthy AI” – resilience, security, transparency, and accountability – into model design and operations. ISO/IEC 42001 complements this with a management-system approach: roles, audits, objectives, and continual improvement.

Adversarial attacks – how attackers target the models themselves

As AI becomes mainstream, more attacks aim at the models rather than only classic IT systems. Adversarial attacks deliberately perturb inputs, poison training sets, or extract models to trigger wrong decisions. Recent NIST publications classify these threats and recommend robustness testing, strict model version control, and including adversarial scenarios in risk assessments. A practical resource is MITRE ATLAS – a public catalog of tactics and techniques for attacking AI systems, analogous to ATT&CK, which helps plan testing and countermeasures.

Explainable AI – transparency instead of a black box

Explainability (XAI) is not just a convenience. It is a prerequisite for trust and effective oversight of automation. The DARPA XAI program and survey literature show that explanation methods help users understand why a model decided as it did, recognize limitations, and calibrate trust correctly – which, in CTI, shortens triage and eases audits. In practice, this means reporting the features that drove a classification and documenting the model’s decision path.

From reactive to predictive – with control

CTI platforms with an AI layer shift from post-factum detection to risk forecasting: they combine exploit trends, dark-web signals, and telemetry to highlight the vulnerabilities most likely to be used in the coming days. Governance defines how to use these forecasts – confidence thresholds, required analyst review, and how to communicate risk to system owners. This way, prediction strengthens patching and hardening without turning into an uncontrolled autopilot.

Orchestration and Automated Response – where to draw the line

Integrating CTI with SOAR enables some responses to run automatically – from host isolation to policy updates. A good governance practice is a human-in-the-loop model for high-impact events and a clear split between fully automated actions and those requiring approval. NIST AI RMF recommends linking automation decisions to business risk and continuously measuring effects – for example, false-positive rates, data drift, and impact on business continuity.

Legal context in the EU – what the rules actually require

  • AI Act – sets harmonized rules for AI systems, including obligations for high-risk systems: data governance, human oversight, detailed technical documentation, and logging. High risk depends on the use case, not the mere label “AI in security.” The Act does not impose a universal XAI mandate on all systems but requires appropriate transparency and documentation according to the risk class.
  • NIS2 – not an AI regulation; it requires essential and important entities to implement cyber risk management, incident response, and reporting. In practice, AI governance should be woven into these processes, but NIS2 itself does not mandate XAI.
  • DORA – for the financial sector, it defines digital operational resilience, testing, third-party risk, and reporting. It does not specify AI-particular obligations, yet AI governance in banks and ICT providers must align with the DORA control framework.

Business evidence – why this matters

Regardless of technology, governance aims to shorten detection and containment times and reduce impact. Data from IBM Cost of a Data Breach 2024 shows that organizations using security AI and automation reduced breach costs and detected and contained incidents faster than those that did not. That does not replace governance – but it strongly supports it.

How to build practical AI Governance for CTI

  1. Data policy and compliance – lawfulness of sources, data minimization, quality control.
  2. Model assessment and testing – pre-deployment validation, adversarial scenarios, version control, and rollback criteria.
  3. Monitoring and metrics – precision, recall, drift, operational impact; alerts on degradation.
  4. Explainability and audit – XAI reports for high-impact incidents; complete technical documentation.
  5. Automation rules – thresholds, who approves, when automation acts; periodic playbook reviews.
  6. Continuous improvement – scheduled re-training, risk reviews, internal and external audits.
    These steps align with NIST AI RMF and let you embed AI into security processes from the start, not bolt it on later.

Summary

AI meaningfully strengthens CTI – but only when accompanied by mature AI governance. Combining NIST and ISO/IEC 42001 with the EU legal context (AI Act, NIS2, DORA) lets you harness predictive AI without losing control over risk, transparency, and accountability. That is the direction worth taking today.