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20/08/2026 5:21 pm
TL;DR: Model variance makes deterministic, non-deterministic and probabilistic AI non-interchangeable in application security, because it affects auditability, control consistency and enforcement reliability, according to Cycode. Commercial code-generation models hallucinate package names at least 5.2% of the time while open-source models reach 21.7%, and the practical implication is that security teams must separate reasoning from enforcement before agentic workflows turn inconsistency into an attack surface.
NHIMG editorial — based on content published by Cycode: Deterministic vs. Non-Deterministic vs. Probabilistic AI in AppSec: Why the Distinction Is Now a Security Control
By the numbers:
- Commercial code-generation models hallucinate package names at least 5.2% of the time.
- Open source code-generation models hallucinate package names 21.7% of the time.
Questions worth separating out
Q: How should security teams use AI without turning it into a control dependency?
A: Security teams should use AI for summarisation, correlation, and prioritisation, then keep containment in deterministic controls such as access policy, segmentation, and revocation.
Q: Why do non-deterministic AI systems create governance risk in security pipelines?
A: Because variance breaks reproducibility.
Q: What do security teams get wrong about confidence scores in AI tools?
A: They often treat confidence as certainty rather than calibration.
Practitioner guidance
- Separate reasoning from enforcement Use non-deterministic models for explanation, synthesis and recommendation, but require deterministic controls for pipeline gates, policy decisions and audit evidence.
- Calibrate confidence thresholds by pipeline Test probabilistic scoring against your own code, dependency and runtime data before allowing scores to influence remediation priority or blocking decisions.
- Treat hallucinated package names as a supply chain risk Block unapproved dependency identifiers, validate package metadata against trusted registries and monitor for model-generated names that do not exist in your approved catalogue.
What's in the full article
Cycode's full article covers the operational detail this post intentionally leaves for the source:
- Side-by-side examples of where deterministic, non-deterministic and probabilistic modes belong in AppSec workflows
- The hybrid architecture behind Cycode's separation of scanning, reasoning and risk analysis across pipeline stages
- The practical framework for matching AI mode to use case in regulated environments and agentic development
- How the platform connects security context across code, infrastructure, identities and runtime activity
👉 Read Cycode's analysis of deterministic, non-deterministic and probabilistic AI in AppSec →
AI mode selection in AppSec: are your controls keeping up?
Explore further
20/08/2026 6:12 pm
Deterministic control, not model sophistication, is what makes AI safe in AppSec: the article is right to frame consistency as a security control rather than a quality preference. Once AI is used in scanning, triage or enforcement, repeatability becomes a governance requirement because auditors need evidence and engineers need stable behaviour. Practitioners should treat determinism as the default property for control points, not as an implementation detail.
A question worth separating out:
Q: How do organisations reduce the risk of AI-generated package hallucinations?
A: Require dependency allowlisting, validate package names against trusted registries and reject any identifier that is not already approved. Teams should also monitor for newly suggested packages in AI-assisted code changes, because hallucinated names can be turned into malicious supply chain targets.
👉 Read our full editorial: Deterministic, non-deterministic and probabilistic AI in AppSec
