Continuous Testing of Grid AI Systems

Grid copilots and maintenance agents change every sprint, while attackers develop new manipulation techniques weekly. A security assessment at launch says little about the model operating months later. PointGuard AI automates adversarial testing and reruns it whenever models, prompts, or tools change, so operators have current evidence about how grid AI behaves.

Launch-time assessments cannot keep pace with model changes and evolving attacks.

• Models, prompts, and tools change after deployment.
• Manual red teaming covers few operational scenarios.
• New injection and jailbreak techniques appear constantly.
• Reliability teams need proof that testing is ongoing.

AI Red Teaming runs repeatable adversarial tests tailored to grid operations.

1. Define grid scenarios. Build probe sets for outage response, maintenance planning, dispatch support, and field guidance, including unsafe-instruction and injection scenarios.

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

3. Review and compare. Inspect exact prompts, responses, and failure rationales, and compare results across versions.

4. Feed remediation. Route failures to owners and map results to OWASP and MITRE ATLAS for security and reliability reporting.

• OWASP Top 10 for LLMs: LLM01 Prompt Injection, LLM04 Data and Model Poisoning, and LLM09 Misinformation, covering manipulated inputs, tampered models, and unsafe operational guidance.
• OWASP Top 10 for Agentic Applications: ASI01 Agent Goal Hijack and ASI06 Memory and Context Poisoning, addressing redirected agents and poisoned memory or context.
• NIST AI RMF: MEASURE evaluates behavior; MANAGE prioritizes fixes; MAP defines test scope; GOVERN requires ongoing testing.