Trent AI Announces AI Security Maturity Model for Agentic Era

Business Wire

Trent AI, an agentic security company, today announced the availability of its AI Security Maturity Model (ASMM). The structured framework helps security teams to assess and improve enterprise security posture and mature organizational security programs for the AI era, while reducing unmanaged risk across AI and agentic systems.

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Trent AI Maturity Curve

Built on the foundation of the NIST Cybersecurity Framework 2.0 and aligned with global standards like the NIST AI Risk Management Framework and the EU AI Act, the model’s assessment evaluates how prepared an organization is to secure its agentic development lifecycle as it migrates to AI-enabled development, enabling the creation of a roadmap and a confirmation to justify funding or resources where maturity is low.

“AI adoption is moving faster than most organizations’ ability to secure it, leaving security to become reactive, driven by incidents instead of strategy,” said Trent AI CEO Eno Thereska. “We created ASMM giving organizations a way to measure, communicate and improve their AI security posture in a systematic way.”

While there is a clear uptick in AI adoption and deployment, nearly 3 in 4 (74%) companies plan to deploy agentic AI within two years, only 1 in 5 (21%) report having a mature model for governance of autonomous agents, according to Deloitte’s 2026 State of AI report. Teams not only lack the governance needed from frameworks, they currently lack the proper tests to judge their security maturity journey, as well as guidance to implement security maturity.

How it works:

The model allows organizations to evaluate across six domains: Govern, Identify, Protect, Detect, Respond and Recover, reflecting the cybersecurity frameworks, but adapted to agentic AI, allowing teams to benchmark their current state and track improvement over time.
Each of the 28 categories gets scored from 1 (Partial) to 4 (Adaptive). The domain scores and overall score tell teams where their program is mature and where the gaps are.
Teams are then given an illustration of the areas that need additional maturity, the ability to create a custom roadmap and a confirmation to justify funding or resources where maturity is low.

“ASMM helps me surface where our traditional security program simply doesn’t see agentic AI risk yet,” said Arvest Bank Principal Security Engineer, Justus Post CISSP, CCSP. “It shows exactly which domains we need to strengthen first, while keeping that uplift aligned to emerging industry requirements and frameworks like NIST CSF 2.0, NIST AI RMF, AI‑CAIQ and the EU AI Act.”

The announcement follows Trent AI’s recent Seed round and launch of its multi-agent security solution designed to secure agents as they evolve. Trent AI’s ASMM also comes on the heels of Trent AIʼs Security Advisor for OpenClaw; a security agent built specifically for providing ongoing security advice while building with OpenClaw.

To learn more about the AI Security Maturity Model and Trent AI, please visit trent.ai.

FAQ:

What separates this model from other security frameworks?
- Trent AI’s ASMM is designed to assess the organization’s security program, not the technical posture of individual systems, and it includes agentic AI systems that reason, act and operate with autonomy.
Does the ASMM apply to all AI systems or only agentic ones?
- It applies to AI systems in scope, then uses the Autonomy Test to determine which systems qualify as agentic for agentic-specific control depth.
How is the ASMM different from NIST AI RMF or ISO 42001?
- NIST AI RMF provides governance principles and ISO 42001 is a certifiable management system standard. Trent AI’s ASMM assesses and tracks your alignment to these frameworks over time.
When will the model be available?
- Enterprises interested in getting an assessment can request it now at Trent.ai
Who is the model for?
- CISOs, security, compliance, governance and risk leaders and engineering teams responsible for AI risk.

About Trent AI

Trent AI is redefining agentic AI with context-driven agentic security. Its proprietary judgement layer and reinforcement learning technology power a collection of specialized security agents. By orchestrating these agents across customer workflows, Trent AI transforms agentic security into an effortless, continuous part of agent development. Trent AI was launched in 2025 by co-founders: Eno Thereska, former Distinguished Engineer at Alcion, AWS, and Confluent, Neil Lawrence, DeepMind Professor of Machine Learning at the University of Cambridge and previous Director of ML at Amazon, and Zhenwen Dai, former Machine Learning Scientist at AWS and Senior Research Manager at Spotify.