Independent AI assurance
Your AI vendor built it. Your integrator deployed it. Who independently validated it?
BlackIndian AI independently tests, attacks, validates, and monitors AI systems before and after deployment.
The category
The independent assurance layer for AI.
No vendor incentives. No deployment ownership. Just a defensible view of how a system behaves.
The missing layer
Builders and deployers have a job to do. Independent validation is a different job.
Builds the AI
Deploys the AI
Independently validates
Receives evidence
Four pillars / one discipline
Evaluate performance.
Measure reliability, groundedness, accuracy, RAG behavior, and requirements against an agreed baseline.
Explore pillarFind weaknesses.
Adversarially test prompt injection, jailbreaks, data leakage, tool abuse, and agent manipulation.
Explore pillarVerify requirements.
Compare expected and actual behavior, then turn findings into evidence your decision-makers can use.
Explore pillarMonitor continuously.
Design telemetry, tracing, regression detection, drift signals, and post-deployment assurance.
Explore pillarBIA-AF™
From discovery through evidence, BIA-AF™ makes independent testing legible to technical, governance, legal, procurement, and mission teams.
See the frameworkUnderstand the system, users, architecture, data, tools, mission, and consequences of failure.
Establish requirements, datasets, benchmarks, expected behavior, and acceptable thresholds.
Conduct adversarial testing against the application, model, agents, retrieval, and tools.
Compare actual behavior against customer-defined requirements and intended use.
Assess monitoring, telemetry, tracing, regression testing, and production signals.
Produce reproducible findings, risk information, recommendations, and retest results.
One assurance layer / two markets
For healthcare, insurance, enterprise, SaaS, and AI organizations deploying LLM, RAG, generative, or agentic systems.
Explore commercial assuranceIndependent TEVV, red teaming, LLM security, agent security, observability, and continuous assurance for government programs and primes.
Government & prime contractorsAssurance Lab
Test objective: Surface retrieval manipulation, data leakage, and grounding failures.
Test objective: Probe tool authorization, unsafe action chains, and boundary failures.
Test objective: Test direct and indirect injection paths across application context.
Clinical, administrative, and patient-facing AI where reliability matters.
Decision systems that require traceable requirements and reviewable evidence.
LLMs, RAG, and agents moving into business-critical workflows.
The decision before deployment
Independent evaluation of performance, security, reliability, and production behavior — before and after deployment.
Request an AI assurance assessment