Governance Advisory
Design governance frameworks, approval workflows, and operating models that help banks move AI initiatives from experimentation to controlled, production-ready deployment with clearer accountability and stronger policy alignment.
Build stronger AI oversight with governance, auditability, and runtime controls designed for banking environments. Trussed AI helps financial institutions align model development, validation, monitoring, and documentation with SR 11-7 expectations, reducing manual review effort while improving traceability, policy enforcement, and exam readiness across AI models, copilots, and agents.
Governance, controls, and assurance services that help banks manage AI risk across the full model lifecycle.
Design governance frameworks, approval workflows, and operating models that help banks move AI initiatives from experimentation to controlled, production-ready deployment with clearer accountability and stronger policy alignment.
Enforce policies in real time across AI models, applications, and agents with centralized controls, audit logging, monitoring, and regulator-ready records built for enterprise oversight.
Apply execution-layer controls to agentic AI so tool calls, data access, and workflow triggers are evaluated against policy before actions occur in banking environments.
Generate continuous audit evidence with complete traces, policy decisions, timestamps, and lineage records that support internal audit, model validation, and regulatory examinations.
Track AI spend by team, workflow, and provider while enforcing budgets and usage thresholds to improve financial control and connect model usage to business outcomes.
Maintain ongoing visibility into AI usage, policy exceptions, and operational performance so banking teams can detect issues early and strengthen continuous model oversight.
Trussed AI helps banks operationalize model risk management with governance embedded directly into AI systems. Instead of relying on static policies and after-the-fact reviews, teams gain real-time enforcement, continuous monitoring, and audit-ready evidence across models, copilots, and agents. The result is a more defensible SR 11-7 program with clearer controls, faster reviews, and stronger visibility into how AI is used, approved, and monitored.
See how regulated organizations improve AI oversight, audit readiness, and operational control with Trussed AI.
Banks need more than policy documents—they need enforceable controls, visibility, and evidence.
Policies are enforced during live AI interactions, not only during periodic reviews.
Every governed interaction creates traceable evidence for internal audit, validation, and examiner review.
Built for regulated environments where SR 11-7 expectations demand documentation, oversight, and continuous monitoring.
SOC 2 Type II and ISO 27001 credentials support stronger security expectations for financial institutions.
Experienced founders building governed enterprise AI infrastructure.
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 includes senior roles at Google Cloud, AWS, Adobe, and PayPal/eBay, where he worked on large-scale infrastructure, reliability, and cost optimization challenges. At AWS, he led product management for Elastic Load Balancing, helping drive broad adoption and operational savings. He also founded Finsphere, later acquired by Visa, where he helped pioneer fraud detection using mobile location data. That blend of infrastructure depth and financial risk innovation informs Trussed AI's approach to governed, production-ready AI. Ajay holds a master's degree in Electrical Engineering and Computer Science from the University of Florida and a Bachelor of Technology from IIT Delhi.
Co-Founder
Branden McIntyre is Co-Founder of Trussed AI and focuses on infrastructure that helps enterprises deploy AI reliably at scale. Across product roles at Rakuten, Cisco, JustAnswer, and Oracle, he saw the same recurring issue: organizations could experiment with AI, but lacked the controls and operational tooling needed for safe production deployment. At Rakuten and JustAnswer, he led AI prediction initiatives that improved customer experience and platform efficiency, giving him firsthand insight into the governance gaps that emerge as models move into real workflows. His work today centers on helping enterprises implement AI systems safely, effectively, and with stronger operational discipline. Branden 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. At JustAnswer, she led initiatives that integrated large language models into core workflows, including copilots, conversational interfaces, and human-in-the-loop systems that improved engagement and accuracy. Earlier roles at Microsoft and Accellion involved product innovation, unified communications, and strategic partnerships with major technology providers. She also holds multiple patents in location-based fraud detection, adding valuable perspective for regulated industries managing risk-sensitive AI use cases. Sunita holds graduate and undergraduate engineering degrees from the University of Maryland and Osmania University.
The OCC commonly references nine broad risk categories for banks: credit, interest rate, liquidity, price, operational, compliance, strategic, reputation, and transaction risk. For AI programs, several of these can overlap at once. Model failures may create operational and compliance issues, while weak governance or poor outputs can also affect reputation, strategic decisions, and customer-facing controls.
Talk with our team about AI governance, controls, and audit readiness.
Independent controls audit for security.
Information security management certification.
Supports structured AI risk governance.
Share your current AI governance goals, model risk challenges, and compliance priorities. Our team will outline practical next steps for SR 11-7 alignment.
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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.