AI governance is still too often framed as a normative exercise rather than an operational discipline.
Principles, ethical commitments, and high-level frameworks are treated as sufficient safeguards. Yet these constructs implicitly assume stable systems, trustworthy inputs, and predictable environments. In real digital ecosystems, none of these assumptions hold. AI systems operate inside infrastructures that are contested, observable by adversaries, and continuously stressed by malicious or accidental interference.

In 2026, AI-enabled decisions are no longer limited to advisory roles. They shape access, trigger automated responses, prioritise risks, and influence outcomes with tangible legal and financial consequences. When governance models are designed without cyber and forensic intelligence, they systematically underestimate how AI behaviour can be altered without changing the model itself (through data poisoning, signal manipulation, environmental degradation, or timing-based attacks). Governance that ignores these vectors governs an idealised system, not the one that actually exists.

Cyber and forensic intelligence introduce a critical epistemic shift into AI governance.
They force organisations to reason about AI not as a rational actor, but as a component embedded in adversarial systems. This perspective reframes governance questions: not only what decisions AI is allowed to make, but under which conditions those decisions remain valid. It asks whether confidence scores, thresholds, and escalation rules still make sense when inputs are partially compromised or when system visibility is degraded.

From a forensic standpoint, governance must also account for post-incident reality. Decisions are not evaluated only in real time, but retrospectively by regulators, auditors, courts, or internal investigators. If an AI-driven action cannot be reconstructed, contextualised, and defended after the fact, governance has already failed, regardless of how compliant it appeared ex ante.

This is why AI governance that lacks cyber-forensic grounding tends to collapse under scrutiny. It produces accountability narratives rather than accountability evidence. Effective governance, by contrast, assumes failure, intrusion, and ambiguity as design conditions. It embeds traceability, contestability, and evidentiary integrity directly into AI-enabled processes. Without this foundation, governance remains aspirational: credible in theory, fragile in practice.

References

ENISA
Adversarial Artificial Intelligence: Threats, Attacks and Mitigations.
ENISA, 2024.

IEEE
IEEE 7000-2021™ Series — Model Process for Addressing Ethical Concerns during System Design.
IEEE Standards Association, edizioni applicative 2023–2024.

MITRE
Adversarial Threat Landscape for Artificial-Intelligence Systems (ATLAS).
MITRE, release 2024.

NIST
Trustworthy and Responsible AI Resource Center — Cybersecurity & AI Risk Integration.
NIST, 2024–2025.

Brundage, M., Avin, S., Clark, J., et al.
The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation.
Oxford University Press, aggiornamenti editoriali 2023–2024.