Adversarial Testing
Simulate prompt injection, jailbreaks, unsafe outputs, and misuse scenarios to expose weaknesses in enterprise LLM applications before they affect users, data, or downstream systems.
Stress-test enterprise LLM deployments before attackers, auditors, or end users do. Trussed AI helps organizations uncover prompt injection paths, policy gaps, unsafe agent behavior, data leakage risks, and governance blind spots across models, copilots, and workflows. Get practical findings, runtime controls, and audit-ready evidence that support safer production rollouts.
Targeted services that identify, validate, and reduce risk across enterprise LLMs, agents, and governed AI workflows.
Simulate prompt injection, jailbreaks, unsafe outputs, and misuse scenarios to expose weaknesses in enterprise LLM applications before they affect users, data, or downstream systems.
Evaluate and constrain agent behavior at execution time, validating tool-use boundaries, workflow permissions, and policy enforcement across multi-agent and API-driven environments.
Generate traceable evidence from governed interactions, helping teams review model behavior, policy decisions, and incident paths with records suited for internal and external audits.
Apply real-time governance, access controls, and guardrails across models, apps, and developer tools so identified red-team findings can be mitigated in production.
Design governance workflows, review processes, and operating models that turn red-team findings into enforceable policies, stakeholder alignment, and production-ready controls.
Assess how model misuse, routing choices, and uncontrolled agent activity affect spend, then enforce thresholds and attribution to reduce financial exposure.
We define the systems under test, business context, threat scenarios, sensitive data paths, and policy requirements across LLM apps, copilots, agents, and developer workflows.
Governance, assurance, and runtime control designed for high-stakes AI deployments.
Trussed AI helps enterprises move from AI experimentation to governed, production-ready deployment.
Policies are enforced in real time across models, agents, and workflows.
Every governed interaction creates traceable evidence for compliance, review, and assurance.
Founders bring deep product and infrastructure experience from AWS, Google Cloud, Adobe, and Microsoft.
Teams can move from governance design to live operational workflows in as little as four weeks.
Experienced leaders in enterprise AI infrastructure and governance.
Co-Founder
Ajay Dankar is Co-Founder of Trussed AI and brings nearly three decades of cloud product and engineering leadership to enterprise AI governance. His background spans Google Cloud, AWS, Adobe, PayPal/eBay, and Visa-acquired Finsphere, where he worked on scale, reliability, fraud detection, and cloud cost optimization. At AWS, he led product management for Elastic Load Balancing, helping drive broad adoption and operational savings. At Trussed AI, Ajay focuses on helping enterprises deploy generative and agentic AI with stronger control, resilience, and governance built into production environments. His experience across public and hybrid cloud systems makes him especially effective in designing infrastructure that can support secure, high-volume AI operations without sacrificing performance or oversight.
Co-Founder
Branden McIntyre is Co-Founder of Trussed AI and focuses on infrastructure that helps enterprises deploy AI reliably at scale. Across roles at Rakuten, Cisco, JustAnswer, and Oracle, he saw the same recurring challenge: organizations could prototype AI quickly, but lacked the tooling and controls needed for safe production deployment. His work leading AI prediction and machine learning initiatives sharpened his understanding of operational risk, system performance, and the gap between experimentation and enterprise readiness. At Trussed AI, Branden applies that experience to building practical governance and control capabilities for LLMs, copilots, and agents. He helps customers create deployment environments where AI systems can be tested, monitored, and managed with greater confidence.
Co-Founder
Sunita Reddy is Co-Founder of Trussed AI, where she leads AI, operations, and partner strategy for enterprise adoption of generative and agentic AI. With more than two decades of experience across product, AI, and design, she has built scalable solutions at JustAnswer, Microsoft, and Accellion. Her background includes integrating large language models into production workflows, launching co-pilot systems, and developing human-in-the-loop AI products that improved engagement, accuracy, and revenue. She also brings deep partnership experience from work with companies such as Verizon, Okta, and MobileIron. At Trussed AI, Sunita helps organizations translate emerging AI capabilities into governed, enterprise-ready systems with the operational structure and partner ecosystem needed for long-term success.
Adversarial testing for generative AI is the practice of intentionally probing an LLM, copilot, or agent with harmful, deceptive, or edge-case inputs to uncover failure modes. Tests often target prompt injection, jailbreaks, unsafe outputs, data leakage, tool misuse, and policy bypasses. The goal is to identify exploitable weaknesses before deployment or before they create security, compliance, or operational incidents.
Talk with our team about testing, governance, and deployment controls.
Validated controls for security and operations.
Recognized information security management standard.
Supports structured AI risk management.
Share your LLM, copilot, or agent deployment goals and our team will outline practical red teaming, governance, and control options.
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To help us assist you faster, please include the reason for your message so the relevant team can reach out as soon as possible.