Reducing AI security review time by 80%

Background

A Series C AI platform company providing LLM-powered document analysis, summarization, and workflow automation to enterprise clients across financial services, legal, and healthcare. With 200+ employees and $45M ARR, the company had built a strong product but was losing deals at the security review stage.

Enterprise buyers in regulated industries required detailed evidence that AI features were tested against prompt injection, data leakage, and hallucination risks. The existing manual penetration testing process took 8-12 weeks per release, and the security team of three could not keep pace with the product roadmap.

Deals worth over $5M in annual contract value were stuck in procurement. Prospects explicitly asked for OWASP LLM Top 10 coverage documentation and NIST AI RMF alignment evidence, none of which the team could produce at scale.

The challenges

Manual testing bottleneck Each new LLM feature required 8-12 weeks of manual review. The three-person team faced a six-month backlog.
Compliance documentation gap No automated way to generate framework-aligned evidence. Ad-hoc PDFs failed procurement scrutiny.
Inconsistent test coverage Manual red teaming covered only the obvious vectors. Multi-turn jailbreaks and indirect injection through document uploads were largely untested.
No regression testing Model and prompt updates shipped without re-running security tests, re-introducing previously fixed vulnerabilities.

Our approach

Baseline assessment

Ran a full 27-technique attack suite against the production API endpoints.

Mapped all LLM-powered endpoints and their input surfaces
Executed prompt injection, jailbreak, and data extraction attacks
Identified 23 vulnerabilities across 4 severity levels
Generated baseline OWASP LLM Top 10 and NIST AI RMF reports

CI/CD integration

Integrated AgenticAssure into GitHub Actions for every PR touching LLM code.

Added security test gates to the deployment pipeline
Configured regression suites per product module
Set up Slack alerts for critical and high-severity findings
Established pass/fail thresholds tied to OWASP framework

Continuous monitoring

Deployed scheduled benchmarks running against staging and production daily.

Automated nightly red-team runs across the full API surface
Drift detection for model behaviour changes
Weekly compliance reports auto-delivered to security leads
Real-time dashboards for executive reporting

Representative findings

System prompt extraction via multi-turn conversation

critical

A four-turn conversation could extract the full system prompt, including business logic and customer classification rules, exposing proprietary pricing models to any authenticated user.

Cross-tenant data leakage through document context

critical

When processing uploaded documents, the RAG pipeline occasionally included chunks from other tenants' documents in the context window. Adversarial prompting could surface that data.

PII extraction from summarization outputs

high

The summarization feature could be coaxed to include verbatim PII from source documents even when the output format explicitly prohibited it.

Hallucinated compliance citations

medium

The compliance report generator occasionally cited non-existent regulatory provisions with high confidence, risking incorrect legal guidance if used unreviewed.

Outcomes

Security review cycles dropped from 12 weeks to under 2, an 80% reduction
Three enterprise deals totalling $2.4M ACV closed in Q1 after months stalled in procurement
Cross-tenant leakage patched within 48 hours of discovery, avoiding regulatory exposure
Automated regression testing caught 7 re-introduced vulnerabilities across 3 model updates
Compliance-ready reports shipped with every release, 90% effort reduction
SOC 2 Type II audit completed 3 months ahead of schedule using AgenticAssure evidence

We had $5M in pipeline stuck behind security reviews. AgenticAssure didn't just find vulnerabilities, it gave us the compliance documentation procurement teams actually accept. We closed three deals in the first quarter after deployment.

VP of Engineering · Enterprise AI Platform Company