AI, LLM, and agent testing authority

NAIGIS Cognitive

NAIGIS Cognitive is the authority layer for AI systems that think, generate, decide, recommend, retrieve, remember, or act. It is designed to test AI and LLM behavior, disclose limitations, detect drift and misrepresentation, require human supervision where needed, and make public trust claims verifiable instead of promotional.

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What this system does

NAIGIS Cognitive governs autonomous AI agents, LLMs, copilots, model chains, tool use, AI-BOM, memory governance, security testing, drift testing, red-team certification, reliability scoring, misrepresentation detection, and public AI safety disclosure.

  • AI agent registry, AI-BOM, model binding, prompt policy, RAG source, tool permission, and memory governance
  • AI security testing for prompt injection, jailbreaks, tool abuse, data leakage, RAG poisoning, and memory poisoning
  • Cognitive drift testing for model, prompt, tool, memory, retrieval, and decision consistency changes
  • Malicious behavior, public harm, manipulation, red-team, and continuous monitoring programs
  • Reliability transparency for completion claims, unsupported capability claims, human supervision, corrections, and retractions
  • Public AI safety disclosure with redacted proof-pack support
Public Knowledge

Plain-language context for public trust.

Auvexra pages are written for public understanding. They avoid internal engineering labels and focus on what people can verify, what the system does, and where users can exercise privacy or account rights.

An AI output is not complete because the AI says it is complete; completion must be supported by evidence, tests, artifacts, and review state.

NAIGIS Cognitive does not certify universal correctness. It certifies a defined AI system, version, use case, permission envelope, and evidence record.

Public users should be told what an AI is approved to do, what it is not approved to do, when human review is required, and how to file a complaint or appeal.

If evidence is missing, the system must fail closed instead of presenting an unsupported safety or completion claim.

Mobile and account coverage

  • NAIGIS Mobile can surface AI safety status, security test status, drift status, reliability grade, public limitations, and correction/retraction history.
  • Sensitive test prompts, exploit details, private customer data, and protected remediation records must remain redacted.
  • Users must have clear privacy, data deletion, complaint, and appeal paths from mobile and web.

Expected outcomes

  • AI systems become accountable operational subjects instead of opaque tools.
  • People can see whether an AI system has known limitations, restrictions, or review requirements.
  • Organizations can reduce consumer misexpectation and public harm by clearly separating verified capabilities from unverified claims.