As enterprises increasingly deploy autonomous AI systems across critical business operations, traditional governance models based on documentation and periodic reviews are becoming insufficient. LatticeFlow AI has introduced a unified platform designed to help organizations continuously evaluate, monitor, and govern AI risk with technical evidence throughout the AI lifecycle.
Quick Intel
A Unified Platform for Continuous AI Governance
LatticeFlow AI has announced a new platform designed to help enterprises manage AI risk in the emerging era of agentic AI. As organizations increasingly deploy autonomous AI systems that continuously evolve through changing models, data, and workflows, the company aims to replace traditional governance methods with continuous, evidence-based monitoring.
The platform unifies AI discovery, evaluation, and governance into a single environment, enabling organizations to identify AI assets, assess performance and security, and manage AI risk across foundation models, enterprise applications, and autonomous AI agents.
"By mapping AI frameworks to technical controls, we enable enterprises to understand, control and govern AI risk with evidence, continuously," said Dr. Petar Tsankov, CEO at LatticeFlow AI.
“AI governance has long lacked a technical foundation,” said Dr. Petar Tsankov, CEO and Co-founder of LatticeFlow AI. “There has been a persistent gap between what governance frameworks require and what organizations can actually measure. By connecting frameworks directly to technical controls, we enable enterprises to understand, control and govern AI risk with evidence, continuously.”
AI Atlas Connects Governance Frameworks to Technical Controls
At the core of the platform is AI Atlas, which LatticeFlow AI describes as the world's first public registry that maps AI governance frameworks directly to technical risk controls and executable AI evaluations.
The registry currently supports more than 40 governance frameworks, including the EU AI Act, NIST AI Risk Management Framework, ISO 42001, OWASP, and FINMA. Organizations can use these predefined evaluations to generate verifiable technical evidence, interpret AI risks, and provide board-level visibility into governance and compliance.
Rather than relying on static documentation, AI Atlas enables organizations to continuously validate AI systems against evolving governance requirements as models and operational environments change.
Continuous Monitoring for the Agentic AI Era
The platform has been designed specifically for organizations deploying agentic AI systems that reason, make decisions, and perform autonomous actions.
Its capabilities include use-case-specific AI evaluations, adaptive red teaming for agentic AI, and continuous monitoring that reassesses systems whenever AI models, enterprise data, or security threats evolve. This approach helps organizations identify emerging risks while maintaining oversight across rapidly changing AI environments.
“AI governance can no longer be treated as a static verification problem,” said Dr. Apostol Vassilev, Leading Expert in Trustworthy and Responsible AI and Cybersecurity at NIST. “Because we cannot build a flawless, permanent wall around AI systems, security and governance must evolve beyond cyclical, paper-driven reviews and move directly into the operational runtime to continuously measure, constrain, and manage risk throughout the operational lifecycle. Only by replacing static policy reviews with continuous, runtime technical evidence can we confidently navigate the realities of the agentic world.”
The importance of continuous governance is also reflected by enterprises operating in regulated industries.
“Agentic AI is fundamentally changing what effective governance requires. As AI systems gain the ability to reason, use tools and take autonomous actions, policies and periodic reviews are no longer enough. Organizations need to continuously discover where AI is being used, evaluate how it behaves, and govern it through technical risk controls that evolve alongside these systems. This is especially critical in banking, where innovation must scale alongside trust, resilience, and regulatory accountability. LatticeFlow AI is making important contributions to the evidence-based control of agentic systems,” said Dr. Holger Harms, Head of Banking Innovation Lab at Swisscom.
Enterprise Adoption and Industry Recognition
According to LatticeFlow AI, the platform is already being used by organizations including SAP, Axpo, Unique AI, and other enterprises operating in highly regulated industries.
The announcement follows the company's recognition in the inaugural 2026 Gartner Magic Quadrant for AI Governance Platforms, highlighting its focus on evidence-based AI governance over traditional compliance-driven approaches.
“Innovation only scales when it's built on trust,” said Dr. Sina Wulfmeyer, Chief Data Officer at fast-growing fintech Unique AI. “As AI moves into core investment and advisory workflows, we need continuous technical evidence that our systems are reliable, transparent and safe. The LatticeFlow AI Platform gives us that evidence, so we can deploy AI with confidence in a highly regulated environment.”
As enterprises continue adopting agentic AI, LatticeFlow AI's platform aims to provide continuous technical evidence that supports responsible AI deployment, governance, and long-term risk management across evolving AI systems.
About LatticeFlow AI
LatticeFlow AI sets a new standard in AI governance through deep technical assessments that enable evidence-based decisions and empower enterprises to accelerate AI adoption with confidence. As the creator of COMPL-AI, the world’s first EU AI Act framework for Generative AI developed with ETH Zurich and INSAIT, the company combines Swiss precision with scientific rigor to operationalize AI governance built on evidence and trust.
