Static scanning misses emergent behaviour, so the organisation can approve code that later behaves outside policy in production. The failure is a mismatch between what was reviewed and what actually runs. Teams end up with technical assurance on the build, but no visibility into runtime drift, tool misuse, or overreach.
Why This Matters for Security Teams
AppSec scanning is effective at finding known flaws in code, but it does not tell a security team how an AI system will behave once it can reason, call tools, retrieve data, or chain actions at runtime. That gap matters because AI risk is not limited to source-code defects. It also includes prompt injection, tool abuse, secret leakage, and over-permissioned execution paths that only appear after deployment.
In practice, the control failure is usually not a missed vulnerability in a repo. It is a misplaced assumption that a clean scan means a safe system. The state of the runtime depends on model inputs, orchestration logic, external APIs, and the identities attached to those calls. NHI Management Group has documented how exposed secrets and AI credential abuse can be exploited quickly in real environments, including the LLMjacking research and the State of Secrets in AppSec findings. External guidance is converging on the same point, as shown in Anthropic Project Glasswing, where runtime controls matter as much as build-time review.
In practice, many security teams encounter AI misuse only after a model has already been placed into production with broad tool access and no runtime guardrails.
How It Works in Practice
AI security must extend beyond static scanning because the system’s dangerous behavior is often emergent. A model can pass AppSec review and still fail in production if it can retrieve sensitive data, invoke internal services, or act on untrusted instructions. That is why current guidance increasingly treats the AI workload as an identity-bearing runtime, not just an application artifact.
Effective controls focus on what the agent can do at request time. That usually means workload identity, short-lived credentials, policy checks at execution time, and explicit limits on tool use. Static code review still has value, but it only validates part of the chain.
- Bind each agent or model runtime to a distinct workload identity so actions are attributable.
- Issue just-in-time credentials with short TTLs instead of long-lived secrets embedded in code or config.
- Evaluate authorization dynamically, based on the specific task, data sensitivity, and destination system.
- Log tool calls, retrievals, and privilege changes as first-class security events.
- Block direct access to secrets stores unless a runtime policy explicitly allows it.
This is where frameworks such as the CSA MAESTRO agentic AI threat modeling framework and the emerging runtime-focused guidance in DeepSeek breach analysis become operationally useful: they push teams toward behavior-aware controls rather than checkbox scanning. AppSec scanning still finds vulnerable libraries and unsafe patterns, but it cannot prove the model will not exfiltrate data through a permitted tool call or escalate through chained actions. These controls tend to break down when autonomous agents are allowed to discover and reuse internal tools because the actual attack path is created at runtime, not in the code review window.
Common Variations and Edge Cases
Tighter runtime control often increases integration overhead, so organisations have to balance security depth against delivery speed. That tradeoff is real, especially when teams are trying to move fast with copilots, chat interfaces, or agentic workflows embedded into business systems.
There is no universal standard for this yet, but current guidance suggests that the more autonomous the system, the less reliable static AppSec becomes as the primary control. A code-scanned AI assistant with read-only access to documentation is a very different risk profile from an agent that can execute API calls, update records, or trigger workflows. The second case demands policy enforcement at runtime, not just defect detection at build time.
Edge cases often appear in environments with shared credentials, broad service accounts, or multiple agents operating in the same trust zone. In those settings, a scan may confirm that the application code is free of obvious flaws while missing the real problem: one compromised agent can inherit the trust of another, and a single privileged token can be reused across many actions. That is why NHI Management Group research on secrets management failure modes is so relevant here. AppSec should remain part of the program, but it cannot be the boundary of AI security when the workload itself can change behaviour after deployment.
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 CSA MAESTRO address the attack and risk surface, while NIST AI RMF set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | A01 | Static scans miss runtime abuse patterns common in agentic systems. |
| CSA MAESTRO | TA-2 | MAESTRO addresses agent threat modeling beyond code vulnerabilities. |
| NIST AI RMF | AI RMF covers operational AI risk that static scanning cannot capture. |
Use AI RMF to govern runtime monitoring, accountability, and post-deploy risk management.
