Gadriel AI Behavioral Assurance (GBA) validates the behavior of AI systems — RAG, LLM workflows, agents, MCP, A2A — before they ship. Pre-production, behavioral evidence that the system is ready to deploy and safe to operate.
Teams building and deploying AI systems — including RAG applications, LLM workflows, agents, MCP, A2A, and AI-powered applications.
Need to prove those systems are accurate, secure, policy-aligned, cost-efficient, and safe to operate — before they create business risk.
Pre-production behavioral validation of AI systems across key dimensions: accuracy, security, authentication, authorization, bias, tool use, agent behavior, and FinOps.
Security-only tools that test one dimension, runtime-only tools that act after risk is already live, or manual testing that is hard to scale and difficult to prove.
Gadriel validates the behavior of AI-powered systems before they ship — giving teams reproducible, audit-defensible evidence that the system is ready to deploy and safe to operate.
Behavioral validation before deployment.
Runtime guardrails — complementary category after pre-flight behavioral validation.
Observability after events occur — complementary category after operation.
COMPLEMENTARY means these categories matter, but they do not replace Gadriel's pre-flight behavioral validation before autonomous AI ships.
No aircraft carries passengers without airworthiness certification first. No autonomous AI should reach production without behavioral validation.
Eight risk pillars, five mathematical methods, one canonical frame. Each pillar is independently scored, evidenced, and reproducible — the same inputs always produce the same outputs.
Adversarial robustness, prompt injection, data exfiltration.
EU AI Act, NIST AI RMF, ISO 42001, SR 11-7.
Harmful output, jailbreak resistance, content boundaries.
Latency, error handling, retry, SLO adherence.
Token economics, runaway cost, budget enforcement.
Instruction following, goal stability, output consistency.
Multi-agent coordination, handoff fidelity, collisions.
Demographic parity, distributional fairness, drift.
Reproducible. Audit-defensible. Same input, same output. Math, not opinion. Cosine similarity, KL divergence, spectral analysis, mutual information, PAC bounds.
Built by 25-year cybersecurity practitioners with three USPTO patents — not VC-prompted founders chasing the AI wave.
Pre-production behavioral validation across eight pillars — not observability, not red-teaming-only. We do not blur.
Measurable assurance signals, not marketing noise. Quiet confidence. Strong claims when defensible. The work speaks.
For platform trust owners, CISOs, Chief AI Officers, and model risk managers preparing autonomous AI for production. Run a scoped 4–6 week Proof of Value against your real AI system.
REQUEST PROOF OF VALUE →GBA validates the behavior of AI-powered systems — RAG, LLM workflows, agents, MCP, A2A — before they ship. It produces reproducible, audit-defensible evidence that the system is ready to deploy and safe to operate.
Guardrails and runtime-protection tools act after risk is already live and typically check a single dimension. GBA validates AI system behavior across eight pillars before deployment — so risk is caught before it reaches users, not just blocked when it appears.
Observability (Arize, LangSmith, Langfuse) is post-flight — it tells you what happened. GBA is pre-flight — it tells you whether the system should fly at all, with reproducible behavioral evidence.
No. GBA uses mathematical validation — cosine similarity, KL divergence, spectral analysis, mutual information, PAC bounds. Same input, same output. Reproducible and audit-defensible. We do not use AI to judge AI.
Security, Compliance, Safety, Operational, FinOps, Coherence, Teamwork, and Bias. Each pillar is independently scored and evidenced.
RAG applications, LLM workflows, single and multi-agent systems, MCP and A2A integrations, and AI-powered applications built on any major model provider.
Platform trust owners, CISOs, Chief AI Officers, Heads of AI, and model risk managers preparing autonomous AI for production in regulated or high-stakes environments.
EU AI Act, NIST AI RMF, ISO 42001, and SR 11-7. Each finding is tagged to the relevant control so the validation report drops into existing governance and audit workflows.
Request a Proof of Value. We scope a 4–6 week pilot against one of your AI systems, run the eight-pillar validation, and deliver a reproducible report you can take to your governance, security, and risk stakeholders.