AI systems create risk through attack paths that do not fit traditional IT assumptions, such as data poisoning, model inversion, model extraction, and adversarial examples. A separate framework is needed because the target is not only infrastructure but also model behaviour, training data, and output integrity. That changes what defenders need to monitor, test, and harden.
Why Traditional Cyber Frameworks Miss AI-Specific Attack Paths
Traditional cyber frameworks are excellent at describing perimeter, endpoint, network, application, and access-control risk, but AI systems also need coverage for training-time, model-time, and output-time failures. The practical gap is that a model can be compromised without any host being breached in the usual sense, so defenders need a framework that treats model behaviour, data provenance, and inference integrity as first-class security objects.
That distinction matters because the attacker may be aiming to distort what the system learns, reveal what it remembers, or manipulate what it says, not just to gain infrastructure access. A framework built for AI has to make those attack surfaces visible so teams can test them directly rather than assuming conventional IT controls will catch them.
What Changes in the Threat Model for AI Systems
AI introduces failure modes that are structurally different from ordinary software abuse. Data poisoning targets the training or fine-tuning corpus, model inversion and extraction target information hidden in the model, and adversarial examples target the system’s decision boundary or output reliability. These are not edge cases, they are core AI security concerns because the asset under attack is the model’s behaviour as much as its surrounding platform.
That changes what “protection” means in practice. You need controls for dataset integrity, prompt and output handling, evaluation of model robustness, monitoring for drift or abuse patterns, and validation that third-party models or tools are behaving as intended. A general-purpose cyber framework can still contribute, but it usually needs AI-specific controls layered on top to describe where the risks begin and how to measure them.
For an overview of AI adversarial techniques, MITRE ATLAS adversarial AI threat matrix is useful because it maps AI attack techniques to realistic defender actions. For AI governance and risk structure, NIST AI Risk Management Framework helps teams organise risk identification, measurement, and control selection around AI-specific failure modes.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Agentic AI Top 10 address the attack and risk surface, while NIST AI RMF set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST AI RMF | AI Risk Management Framework | AI-specific risk governance covers model, data, and output integrity threats. |
| Recommendation — Apply AI RMF processes to identify, measure, and manage model and data risks beyond standard IT controls. | ||
| OWASP Agentic AI Top 10 | Agentic AI Threats | Agent and tool abuse, prompt injection, and output manipulation are core AI security concerns. |
| Recommendation — Use agentic AI controls to constrain tool use, validate prompts, and bound autonomous actions. | ||
Practitioner Guidance
What to prioritise: Treat data lineage, model access, and output integrity as separate control planes. If you only harden the hosting stack, you will miss poisoning, extraction, and prompt-driven abuse that never looks like a classic infrastructure compromise.
What to verify: Confirm you can test the model’s behaviour under adversarial inputs, trace training and fine-tuning data provenance, and detect whether outputs are drifting, leaking, or being manipulated. If you cannot observe those states, your security model is incomplete.
Common mistake: Using ordinary application security checklists as a substitute for AI threat modelling. That usually overweights authentication and infrastructure hygiene while underweighting the integrity of the data and model itself.
Practitioner takeaway: The right framework is the one that lets defenders ask AI-native security questions, not just traditional IT questions, because the most important failure may be a corrupted model outcome rather than a breached server.
Related resources from NHI Mgmt Group
- Why do agentic AI systems break traditional compliance frameworks?
- Why do AI systems need threat modelling and security controls earlier than traditional software projects?
- How does the rise of AI identities impact traditional IAM systems?
- When does AI create more governance risk than traditional data systems?
