Continuously Test Models for Adverse Outcomes

Underwriting and pricing models change with every release, and new manipulation techniques appear weekly. A red-team review at launch says little about the model running in production months later. PointGuard AI automates adversarial testing and reruns it whenever models, prompts, or tools change, giving carriers current evidence about how their AI behaves.

One-time testing cannot keep pace with model updates and evolving attack techniques.

• Manual red teaming is slow and covers few scenarios.
• Model, prompt, and tool changes can introduce new failures.
• Findings are hard to reproduce or compare across versions.
• Governance teams need proof that testing is ongoing.

AI Red Teaming runs repeatable adversarial tests and ties results to the systems and versions they cover.

1. Define insurance scenarios. Build probe sets for underwriting, pricing, claims, and customer service tasks, including security, harmful-output, and bias scenarios.

2. Automate testing. Run tests on a schedule and trigger them automatically when a model, prompt, or tool changes. Cover agents as well as models.

3. Review findings. Inspect exact prompts, responses, and failure rationales. Compare results across versions to spot regressions.

4. Feed remediation and governance. Route failures to owners and track fixes. Map results to OWASP and MITRE ATLAS for governance reporting.

• OWASP Top 10 for LLMs: LLM01 Prompt Injection, LLM07 System Prompt Leakage, and LLM09 Misinformation, covering failures that adversarial probes surface as models and prompts change.
• OWASP Top 10 for Agentic Applications: ASI01 Agent Goal Hijack and ASI06 Memory and Context Poisoning, addressing agents redirected by manipulated inputs or stored context.
• NIST AI RMF: MEASURE evaluates model behavior; MANAGE prioritizes fixes; MAP defines test scope; GOVERN requires ongoing testing.