Reducing AI security review time by 80%

Background

This composite scenario follows a Series C AI platform company providing LLM-powered document analysis, summarization, and workflow automation to enterprise clients across financial services, legal, and healthcare.

Enterprise buyers in regulated industries require detailed evidence that AI features were tested against prompt injection, data leakage, and hallucination risks. A manual penetration testing process taking 8-12 weeks per release can stall roadmap velocity.

Prospects may ask for OWASP LLM Top 10 coverage documentation and NIST AI RMF alignment evidence that manual processes cannot produce at scale.

The challenges

Manual testing bottleneck Each new LLM feature requires 8-12 weeks of manual review. A small security team can face a six-month backlog.
Compliance documentation gap No automated way to generate framework-aligned evidence. Ad-hoc PDFs may fail procurement scrutiny.
Inconsistent test coverage Manual red teaming covers only the obvious vectors. Multi-turn jailbreaks and indirect injection through document uploads are often untested.
No regression testing Model and prompt updates can ship without re-running security tests, re-introducing previously fixed vulnerabilities.

Our approach

Baseline assessment

Run a full attack suite against production API endpoints.

Map all LLM-powered endpoints and their input surfaces
Execute prompt injection, jailbreak, and data extraction attacks
Identify vulnerabilities across severity levels
Generate baseline OWASP LLM Top 10 and NIST AI RMF reports

CI/CD integration

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

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

Continuous monitoring

Deploy 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.

Illustrative outcomes

Security review cycles could drop from 12 weeks to under 2 with continuous testing
Procurement-ready compliance reports shipped with every release
Cross-tenant leakage remediated and verified with regression suites
Automated regression testing catches re-introduced vulnerabilities across model updates
SOC 2 Type II evidence assembled from platform outputs rather than manual reconstruction

Illustrative practitioner perspective (composite scenario)

Continuous testing does not just find vulnerabilities. It produces the compliance documentation procurement teams actually accept.

Illustrative composite based on OWASP LLM Top 10 and NIST AI RMF control requirements