SAST and SCA miss the parts of AI risk that emerge during execution. They can inspect code and dependencies, but they cannot reliably model prompt injection, agent behavior, data flow, or the way valid inputs combine into an exploit chain. The result is a false sense of coverage, especially when unsafe behavior appears only after deployment.
Why This Matters for Security Teams
Relying only on SAST and SCA creates a blind spot that is especially dangerous in AI-powered applications because the highest-risk failure modes often appear after the code has been approved. Static analysis can still be useful for catching insecure code paths, vulnerable libraries, and dependency drift, but it does not evaluate how a model responds to hostile prompts, how an agent chains tools, or how runtime context changes the security outcome. That gap matters because AI systems can turn ordinary application inputs into security events without any source code change.
This is why guidance from NIST Cybersecurity Framework 2.0 remains relevant here: security outcomes depend on governance, protection, detection, and response across the full lifecycle, not just pre-deployment code review. For AI-enabled systems, teams also need to think about model provenance, prompt handling, output validation, and the security of tool-connected workflows. The common mistake is assuming that traditional application security tooling can be stretched to cover model behavior without any runtime controls or adversarial testing. In practice, many security teams encounter prompt abuse and unsafe agent actions only after a production incident has already shown where static scanning stopped short.
How It Works in Practice
SAST examines source code for patterns that look unsafe. SCA checks third-party dependencies for known vulnerabilities and licensing issues. Those are still necessary, but they only describe part of the attack surface. AI-powered applications introduce additional paths that depend on inference-time behavior, retrieved content, conversation history, tool permissions, and the trust placed in model output. A secure review therefore has to extend into runtime validation and abuse-case testing.
For practitioners, that means security testing should include prompt injection scenarios, agent permission boundaries, retrieval poisoning checks, and output filtering or approval gates where the application can trigger real-world actions. Frameworks such as MITRE ATLAS are useful for mapping how adversarial tactics apply to AI systems, while OWASP Top 10 for Large Language Model Applications helps teams think through threats such as excessive agency, insecure output handling, and supply chain risks around models and plugins. For organisations building agentic workflows, controls should also cover tool authorization, least privilege, transaction confirmation, and logging that can reconstruct why the system took a given action.
- Test prompts as an attack surface, not only as user input.
- Validate model outputs before they are trusted by downstream systems.
- Limit tool access so an agent cannot exceed its intended business role.
- Monitor retrieval sources, plugins, and model updates for integrity changes.
- Correlate AI events with SIEM and incident response workflows.
Static scanning should be paired with dynamic abuse testing, runtime policy enforcement, and review of data flows between the model, the application, and external tools. These controls tend to break down when AI features are wired directly into privileged workflows because the application treats model output as trustworthy input without a final validation step.
Common Variations and Edge Cases
Tighter AI controls often increase delivery overhead, requiring organisations to balance speed of release against the cost of deeper testing and governance. That tradeoff is real, especially in fast-moving product teams, but the answer is not to replace runtime controls with more SAST rules. Best practice is evolving, and there is no universal standard for how much testing is enough for every AI use case.
For low-risk assistive features, static review plus lightweight output checks may be acceptable if the model cannot execute actions or access sensitive data. For agentic systems, customer-facing copilots, and workflows that can read secrets or trigger transactions, the bar should be much higher. In those cases, teams should consider whether human approval, scoped credentials, and stronger policy enforcement are required before deployment. NIST’s AI guidance in the AI Risk Management Framework is helpful for separating model risk from ordinary application risk, while OWASP guidance for LLM applications remains practical for day-to-day threat modelling.
The edge case that often gets missed is the “safe code, unsafe orchestration” problem: the application looks clean in SAST, the dependencies are current in SCA, and the failure still appears because an AI component was allowed to infer, retrieve, or act beyond its intended trust boundary. That is where security teams need runtime observability, explicit policy controls, and a clear ownership model for model behavior.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
MITRE ATLAS and OWASP Agentic AI Top 10 address the attack and risk surface, while NIST CSF 2.0, NIST AI RMF and NIST AI 600-1 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST CSF 2.0 | ID.RA-1 | AI runtime misuse is a risk that static scanning will not surface. |
| NIST AI RMF | AI RMF addresses model risk, governance, and lifecycle oversight beyond code scanning. | |
| MITRE ATLAS | AML.TA0001 | ATLAS maps adversarial tactics that SAST and SCA cannot evaluate. |
| OWASP Agentic AI Top 10 | A1 | Agentic AI threats arise when tool access and model output are trusted too much. |
| NIST AI 600-1 | GenAI profile guidance applies to prompt handling and output trust issues. |
Add AI abuse scenarios to risk assessment and verify controls across the full lifecycle.
