AI Control Plane
Centralize AI privacy governance with runtime policy enforcement, audit logging, usage visibility, and deployment options that fit enterprise security and compliance requirements.
Help your organization operationalize AI privacy compliance with real-time controls, audit-ready evidence, and policy enforcement built into AI workflows. Trussed AI supports teams moving from experimentation to production with governance that aligns to CCPA and evolving state privacy requirements, reducing manual oversight while improving visibility across models, agents, and applications.
Explore core platform capabilities that help enterprises govern AI privacy, risk, and compliance in production.
Centralize AI privacy governance with runtime policy enforcement, audit logging, usage visibility, and deployment options that fit enterprise security and compliance requirements.
Control agent actions before tool calls, data access, and workflow triggers execute, helping teams enforce privacy boundaries across multi-agent systems in real time.
Generate continuous audit evidence for governed AI interactions, including policy results, timestamps, model versions, and data lineage for internal and external reviews.
Track and control AI spend across teams, providers, and workflows with real-time attribution, budget thresholds, and routing decisions tied to business outcomes.
Design governance strategies, approval workflows, and operating models that help enterprises align AI deployment with privacy, risk, and regulatory obligations.
Connect existing AI tools, cloud environments, and developer workflows through integrations that extend governance and privacy controls without disrupting operations.
Trussed AI helps enterprises move beyond static privacy documentation by enforcing AI governance where interactions actually happen. The platform applies policy controls across models, agents, applications, and developer tools, while generating audit-ready records automatically. For organizations navigating CCPA and emerging state AI privacy rules, this creates a practical path to stronger oversight, faster reviews, and more confident production deployment.
See how regulated organizations strengthen AI oversight, audit readiness, and operational control.
Built for enterprises that need enforceable AI privacy and governance controls in production.
Policies are enforced in real time across models, agents, and workflows, not checked after deployment.
Every governed interaction creates traceable evidence for privacy reviews, internal audit, and regulatory examination.
Built on controls aligned with SOC 2 Type II and ISO 27001 expectations for enterprise trust.
Teams can move from fragmented oversight to live governance 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, and eBay, where he worked on scaling, reliability, and cost optimization challenges central to production systems. At AWS, he led product management for Elastic Load Balancing, helping drive adoption and operational efficiency. He also founded Finsphere, later acquired by Visa, where he helped pioneer fraud detection using mobile location data. At Trussed AI, Ajay focuses on helping enterprises deploy generative and agentic AI with stronger governance, resilience, and operational control. He holds a master's degree in Electrical Engineering and Computer Science from the University of Florida.
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 firsthand how organizations struggled to bridge the gap between AI experimentation and production deployment. His work leading AI prediction initiatives improved customer experience and platform efficiency while revealing the need for stronger operational tooling and governance. At Trussed AI, Branden applies that experience to building systems that help organizations implement AI safely, effectively, and with greater confidence. His perspective combines product strategy with practical deployment insight, especially for teams moving from pilots to business-critical AI workflows. He holds an MBA from UC Berkeley Haas and a Master of Science from New York University.
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 specializes in turning emerging technologies into scalable enterprise solutions. Her background includes leadership roles at JustAnswer, Microsoft, and Accellion, where she worked on AI products, conversational systems, unified communications, and strategic partnerships. She has helped integrate large language models into core workflows and build human-in-the-loop systems that improve engagement, accuracy, and revenue. At Trussed AI, Sunita focuses on identifying high-impact use cases and helping organizations operationalize AI with stronger governance and partner alignment. She holds a Master of Science in Computer Engineering from the University of Maryland.
A CCPA and state AI privacy compliance platform helps organizations apply privacy, governance, and risk controls directly to AI systems in production. Instead of relying only on policies or manual reviews, it can enforce rules at runtime, monitor usage, generate audit trails, and provide visibility into how models, agents, and applications handle data across workflows.
Talk with our team about your AI privacy requirements.
Independent controls validation for enterprise trust.
Recognized information security management standard.
Supports structured AI risk governance.
Share your AI governance goals, deployment environment, and compliance priorities. Our team will help you evaluate the right platform approach for privacy, audit readiness, and runtime control.
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