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  1. Follow Along with Follow the Winners (NeurIPS 2026)

    Follow the Winners (FTW), our NeurIPS 2026 paper: RL fine-tuning for LLM agents with no repeated attempts and no second model, just a buffer of winners.

  2. What Is AI-SPM (AI Security Posture Management)?

    AI-SPM (AI security posture management) explained: what it covers, how it compares with CSPM, DSPM and ASPM, and the main AI-SPM vendors in 2026.

  3. 16 Best AI Security Tools and Platforms in 2026

    Compare 16 AI security tools and platforms for 2026 by category: agent security, AI posture management, data protection, model supply chain and guardrails.

  4. Building Trent’s agent-native product surface

    How Trent moved coding agents from a local MCP package to a hosted server and plugin, and what it taught us about when an agent should ask or act.

  5. MCP Security: A Threat Model for Agent Tool Access

    Connection auth is where MCP security starts, not ends. Tool poisoning, rug pulls, config secrets, and the local and remote split in Claude Code.

  6. DDoSing the Human in an AI Agentic System

    AI agents operate at machine speed, but human judgment does not. Explore the emerging risk of human DDoS attacks and how to protect the human layer.

  7. Improved Ways to Work With Your AI Security Engineer

    A new experience for Trent’s AI Security Engineer: one project from Understand to Plan to Secure, chat beside every page, and in-place regrades.

  8. 7 AI Threat Modeling Frameworks Compared (2026)

    Compare STRIDE, ATT&CK, ATLAS, MAESTRO, SAILORS, PHANTOM-B and AGENTIC in 2026, and learn how to build a layered AI threat modeling strategy.

  9. The Missing Layer in AI Security: Introducing the ASMM

    The ASMM helps organizations assess AI security maturity, identify risks in agentic AI systems, and build a roadmap to secure AI-driven software development at scale.

  10. The Missing Ingredient in AI: Learning When, Not Just How

    AI already knows how to solve hard problems. The missing ingredient is knowing when to act. Learn the information-judgment gap, and why defensive security is AI’s real frontier.

  11. The Judgment Layer: Rethinking AI Safety for Agentic Systems

    As AI agents become more autonomous, safety depends on preserving human judgment. Learn why the judgment layer is essential for secure agentic AI.

  12. Your Developers Outnumber Your Security Team 80 to 1

    AI has fundamentally changed the economics of software security. As developer output accelerates, security teams can’t keep pace through hiring alone.

  13. Is OpenClaw Safe? Risks, CVEs & Hardening

    It depends on your deployment. A practical security assessment of OpenClaw’s real risks and the controls that make it safe to run.

  14. What Is RAG Security?

    Most RAG pipelines ship without a security model. Learn the threat taxonomy, OWASP LLM08:2025 risks, and how to secure each phase of your stack.

  15. Trent AI Provides Continuous Security Advice for Claude Code Builders

    Trent’s Security Advisor for Claude Code, a security agent built specifically for providing ongoing security advice while building with Claude Code. Many of our customers and design partners tell us that building with Claude Code is incredibly powerful. It can generate secure code, develop implementation plans, and guide you through execution. And Claude Code (and … Trent AI Provides Continuous Security Advice for Claude Code Builders

  16. Notes from CHI 2026: HCI, AI & the Future of Agentic Security

    A week of sessions, a designathon, and a lot of conversations about the impact of AI. Here’s what stuck with me and why it matters for how we design agentic systems. We kicked off CHI 2026 with a designathon meet-up; I helped co-organize: Generative Design and Vibe Coding: Rethinking The Design-Development Divide for UI Prototyping … Notes from CHI 2026: HCI, AI & the Future of Agentic Security

  17. A Report From The India Summit: Redefining How We Build

    From AI sovereignty to secure system design, this report from the India Summit examines why human judgment, governance, and engineering fundamentals matter more than ever.

  18. Trent AI Raises $13M to Rebuild Security for the Agentic World

    Today, we’re excited to share that Trent AI has raised $13M in seed funding to build security for the agentic world. Our CEO shares the story behind why we started Trent.

  19. Trent AI Raises $13M to Secure the Agentic Age

    Trent AI emerges from stealth with a unified agentic security platform and $13M in seed funding to help teams continuously scan, judge, mitigate, and evaluate risk across AI agents and autonomous systems.

  20. Trent AI Provides Continuous Security Advice for Lovable Builders

    Security shouldn’t start at launch. It should evolve with your product. Today we’re excited to introduce Trent’s Security Advisor for Lovable. An AI security agent purpose-built for Lovable builders shipping real products.

  21. What Are LLM Guardrails?

    LLM guardrails are runtime security controls protecting AI applications from prompt injection, data leakage, and unauthorized actions. OWASP mapping, AI firewall comparison, and decision framework.

  22. Trent AI Joins OWASP as Partner Startup

    Trent AI joins OWASP as a startup member to help secure agentic systems, applications, and workloads. Learn how we’re advancing protection against emerging AI threats.

  23. Cybersecurity is Changing Faster Than You Think

    Security is shifting from infrastructure control → autonomous system control. We’re living it every day at Trent AI, and so are the AI-native startups and enterprises we work with.

  24. What Is Threat Modeling?

    Threat modeling is a structured process for identifying and prioritizing potential security threats to a system, then defining countermeasures to prevent or mitigate those threats. It answers four key questions originally framed by Adam Shostack…

  25. Why Reinforcement Learning Matters for Software Security

    I work with an AI coding assistant daily. The striking thing is that it already “knows” everything it needs to get things right on the first try (debugging techniques, architectural patterns, security best practices) yet it can’t apply that knowledge reliably without my steering. That points to a deeper distinction between information and judgment, and it’s exactly the kind of gap reinforcement learning is meant to narrow.

  26. What Is Agentic AI Security?

    Agentic AI systems can act, not just respond. Learn how to secure autonomous AI using STRIDE threat modeling, OWASP LLM guidance, and real-world attack scenarios.

  27. Scaling an AI Startup with AI: An Ops Perspective

    What does Operations really look like at an early-stage AI startup? Trent AI’s Head of Operations shares lessons on building trust, lightweight structure, and scalable systems from day one.

  28. Agents and the Hidden Cost of Making Things Easier

    A systems view on AI agents: how individual productivity gains create hidden costs, fragile workflows, and long-term organizational risk.

  29. Startups Will Announce Breakthroughs That Rival Big Tech

    Artificial intelligence breakthroughs are increasingly coming from startups, not Big Tech, as AI lowers the cost of experimentation and innovation.

  30. Solving Security Problems With AI And Experience

    How do you know when it’s time to start a new company? “I got a text saying, ‘Zhenwen and I quit our jobs. Things are getting real now’ and within a month we’d decided on our name,” said Neil Lawrence, Co-Founder and Chief Scientist at Trent AI.

  31. Speed Isn’t Productivity: Why AI Makes the Hard Work Invisible

    Over the last year, I’ve found myself circling the same unease in different conversations, with different people, in different contexts. It comes up when talking about large language models, about agents, about productivity, and inevitably about security.

  32. It’s Time For A New Way To Develop Secure Software

    70% of security defects start in design, yet most teams still treat security as a pre-launch checklist. Here’s why the traditional approach is broken.

  33. How My Favorite Interview Questions Became My Day Job

    The interview question I asked 100+ candidates became reality: what happens when you’re the first security engineer at a startup with 3 months to launch?

  34. Agents, Checklists, and the New Architecture of Intelligence

    LLMs aren’t replacing infrastructure, they’re intensifying its use. Why the future of AI depends on architecture and security, not smarter models.

  35. Can We Trust AI to Secure Our Code?

    Before you trust AI to secure your codebase, know this: today’s best models detect just 12.5% of real-world vulnerabilities without guidance.

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