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How does Tumeryk perform automated AI red teaming?

Learn how Tumeryk uses automated adversarial testing to identify security, privacy, reliability, safety, and agentic risks in AI systems.

Tumeryk uses automated AI red teaming and risk simulation to evaluate how AI systems behave when subjected to adversarial and risk-focused testing.

Rather than relying only on documentation, questionnaires, vendor claims, or conventional AI capability benchmarks, Tumeryk actively tests AI systems to generate technical evidence about their security, reliability, privacy, safety, and governance posture.

Testing can evaluate AI-specific failure modes including:

  • Prompt injection
  • Jailbreak resistance
  • System-prompt leakage
  • Sensitive-information disclosure
  • Privacy leakage
  • Hallucination
  • Unsafe outputs
  • Agentic boundary violations and excessive agency

Tumeryk evaluates observed test outcomes to identify where an AI system demonstrates resilience and where weaknesses may require remediation or additional controls.

A key empirical input used within the AI Trust Score™ assessment approach is Attack Success Rate (ASR) — the proportion of adversarial attempts that successfully cause an unsafe or undesirable response.

Identified failures can also be evaluated according to their potential impact rather than treating every failure as equally significant. This creates a more risk-sensitive assessment than a simple pass/fail benchmark.

The resulting technical evidence contributes to AI Trust Score™, which translates multidimensional AI risk into a standardized 0–1000 measure of AI trust and provides visibility into specific areas of risk.

Because AI behavior can change as models, prompts, retrieval sources, applications, permissions, and configurations evolve, assessments can be repeated to identify changes in an AI system’s risk posture over time.

Automated red teaming therefore forms part of Tumeryk’s broader continuous governance cycle:

Assess → Measure → Enforce → Monitor → Reassess

This enables organizations to use adversarial testing not only to identify AI vulnerabilities, but also to inform remediation, security controls, runtime guardrails, and ongoing governance decisions.