Late-stage scans inspect artifacts after the design decision is already made, so they often catch consequences rather than influence choices. In fast agent-assisted workflows, that timing gap becomes the main failure mode because the implementation may be mostly complete before security gets a meaningful chance to intervene.
Why This Matters for Security Teams
Late-stage code scanning is valuable, but it is not a substitute for security decisions made earlier in the AI software development lifecycle. By the time a scan runs, the model choice, data flow, prompt handling, tool permissions, and deployment pattern may already be locked in. That matters because many AI security failures are architectural: insecure RAG sources, overbroad agent tools, weak secrets handling, and poor output validation are design problems, not just code defects. The NIST Cybersecurity Framework 2.0 remains useful here because it pushes teams to govern, identify, protect, detect, respond, and recover across the full system lifecycle rather than at the final build gate.
Security teams also miss that AI pipelines change quickly. A harmless-looking integration can become a control bypass when a model gains access to internal documents, external APIs, or sensitive prompts through an agent workflow. Current guidance suggests shifting security left and earlier still into design review, dataset governance, and model provenance checks. In practice, many security teams encounter the real failure only after an agent has already exposed data, taken an unsafe action, or amplified a bad decision, rather than through intentional review of the AI SDLC.
How It Works in Practice
The practical problem is timing. Traditional application security assumes code is the main asset to inspect, but AI systems add dependencies that are often more important than the code itself: training data, prompt templates, model endpoints, retrieval sources, orchestration logic, and tool connectors. A late scan may identify a vulnerable library or a hardcoded secret, yet still miss the fact that the agent can reach a production system with insufficient approval boundaries. The useful security question is not only “is this code vulnerable?” but “what can this system decide, access, retrieve, or execute?”
Teams that want earlier signal should align reviews to AI-specific controls and threat models. NIST’s AI risk guidance and the OWASP Top 10 for Large Language Model Applications both point to prompt injection, insecure output handling, and excessive agency as recurring risks. A practical workflow usually includes:
- design-time review of AI use cases, trust boundaries, and allowed actions
- data lineage checks for training, fine-tuning, and retrieval sources
- approval controls for tool access, secrets, and external integrations
- validation of model outputs before they trigger downstream actions
- logging and monitoring for prompt abuse, unusual retrieval, and tool misuse
Where agentic AI is involved, the identity layer becomes part of the security model. Non-Human Identity governance matters because every agent, service account, API key, or token represents a delegated authority boundary. If those credentials are created late, shared loosely, or rotated without ownership, scanning code will not reveal the operational risk. The CISA Secure by Design guidance is relevant because it reinforces reducing insecure defaults and designing for safer operation before release. These controls tend to break down when AI systems are assembled from multiple low-code services, because ownership, logging, and approval paths become fragmented across teams and platforms.
Common Variations and Edge Cases
Tighter pre-deployment review often increases delivery overhead, requiring organisations to balance speed against the need for earlier risk decisions. That tradeoff is real, especially in fast-moving product teams, but the cost of missing an architectural issue is usually higher than the cost of an extra design review. Best practice is evolving, and there is no universal standard for exactly where every AI control must sit in the SDLC, particularly for agentic workflows and hybrid human-plus-model processes.
Some environments also create exceptions. For internal experimentation, a late scan may be acceptable if the model has no production data, no external tools, and no customer impact. For regulated or high-impact systems, that same approach is much weaker because the harm comes from misuse, unsafe outputs, or unauthorized actions rather than conventional code flaws. The MITRE ATT&CK and MITRE ATLAS resources help teams think about adversarial behavior patterns, while OWASP’s LLM guidance helps explain why prompt and tool abuse often evade traditional scan gates. The answer changes again when the AI system is embedded in a broader cloud stack, because the security issue may sit in identity, orchestration, or data access rather than in the application repository itself.
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 and MITRE ATLAS 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 | GV.RR | AI SDLC security needs governance and lifecycle ownership, not just release-time scans. |
| NIST AI RMF | GOVERN | The core failure is governance timing across model, data, and deployment decisions. |
| OWASP Agentic AI Top 10 | Agent tool use and output handling create risks late scans often miss. | |
| MITRE ATLAS | Adversarial AI threats often target prompts, retrieval, and model behavior, not just code. | |
| NIST AI 600-1 | GenAI profile guidance maps directly to prompt, output, and deployment controls. |
Assign lifecycle accountability early so AI security decisions happen before implementation hardens.
Related resources from NHI Mgmt Group
- Why do flat AI asset inventories miss the real security risk?
- How should security teams govern AI-generated code in production environments?
- How should security teams govern S3 access for sandboxed AI code interpreters?
- How should security teams govern AI code assistants that have repository and cloud access?
