Hallucination and accuracy assurance
Questions to ask
- When your agents write findings, how do you prove they are not hallucinated and are accurate against the actual architecture?
- Do you ground LLM findings in deterministic checks (graphs, CVE–SBOM matching, AppSec findings, and similar signals) so they are not pure hallucination?
- How do you detect model drift when the underlying LLM is updated?
Our differentiator
SecureShift AI treats the LLM as a reasoning assistant inside a controlled pipeline: structured threat models, evidence-backed claims, multi-pass validation, and human-in-the-loop gates. Findings are not free-text hallucinations; they are traceable assertions with confidence scores and provenance. We also track false positives over time so accuracy improves with every review cycle, not just at launch.