Eno Thereska

Eno Thereska co-founded Trent AI in London in 2025 and is its chief executive. He was previously a distinguished engineer at Alcion, which was acquired by Veeam, and at Amazon Web Services (AWS) and Confluent. His co-founders are Neil Lawrence, DeepMind Professor of Machine Learning at the University of Cambridge and a former director of machine learning at Amazon, who is chief scientist, and Zhenwen Dai, the chief technology officer, who previously worked at AWS and Spotify.

Security for software that acts on its own

Trent AI builds security for AI agents and autonomous workflows. Its platform runs specialized agents in parallel. Some scan code, dependencies, cloud infrastructure and runtime behavior; others judge which risk signals matter, propose fixes as pull requests and configuration changes, and check that the fixes worked. The company describes the result as working “like an AI security engineer”.

Thereska set out the gap when the company launched: “Organizations are deploying AI agents and autonomous workflows faster than their security can adapt, and most development teams using these agents and workflows have no security framework designed for their systems.”

A $13m seed round

Trent AI came out of stealth on 7 April 2026 with a $13m (£9.7m) seed round led by LocalGlobe and Cambridge Innovation Capital. Angel investors include Joaquin Quiñonero Candela of OpenAI, Ippokratis Pandis of Databricks and Tony Jebara, formerly of Spotify. Early design partners include Canopy, Commscentre, ML@Cam, Qbeast and Weblogic.

In May 2026 the company published an AI Security Maturity Model, a framework that scores organizations from one to four across 28 categories in six domains, from govern to recover, to measure how ready they are to secure agentic AI. “AI adoption is moving faster than most organizations’ ability to secure it, leaving security to become reactive, driven by incidents instead of strategy,” Thereska said.

Why it matters to UK technology buyers

Trent AI’s premise is that AI agents need their own security controls. As UK organizations connect agents to code repositories, cloud accounts and business data, the risk shifts to what those agents can reach and do. Trent AI gives engineering and security teams a way to assess that exposure and fix it inside the workflow developers already use, and the maturity model gives buyers a way to benchmark where they stand before they choose tools.

Thereska and his co-founders bring large-scale cloud engineering and Cambridge machine learning research to a problem that is only a few years old. The maturity model is an early bid to shape how buyers measure it.