Trustible won the Disruptor Award for Workflow, Agentic AI and Low-Code/No-Code, a new category for 2027, for its lifecycle approach to AI governance for enterprises.
Chartis Research has named Trustible, the AI governance platform built for enterprise AI, a Disruptor in the RiskTech100® 2027 rankings. Trustible won the Disruptor Award in the Workflow, Agentic AI and Low-Code/No-Code category. RiskTech100 is Chartis Research’s annual ranking of risk and compliance technology providers.
Enterprises are adopting AI faster than their governance teams can review it. Teams that once tracked a handful of models now oversee hundreds of use cases, third-party AI tools and a growing number of autonomous agents. Most are still doing it with spreadsheets, intake forms and email threads.
Trustible's skill in helping organizations deal with the growing challenge of AI governance at scale earned the company its designation as a Disruptor in Chartis' RiskTech100® 2027. Its lifecycle approach, including bringing inventories and risk assessments together into common frameworks, is becoming increasingly relevant as generative and agentic AI broaden the scope of model and technology risk.
Philip Mackenzie, Senior Research Principal at Chartis Research
What the Chartis RiskTech100® award recognizes
The category name lines up with three parts of the Trustible platform.
Workflow. Trustible’s Automated Workflows run AI use case intake, risk and impact assessments, vendor reviews, periodic reviews and substantial modifications. Role-based routing shows contributors, reviewers and approvers only what each of them needs. Every use case that goes through intake gets a record in the AI Inventory, and later risk assessments, vendor reviews and compliance reports all draw on that record.
Agentic AI. Trustible’s governance agents handle intake automation, intelligent triage, document analysis and workflow orchestration. They follow rules the governance team can read and edit, log every action they take, and leave final decisions to human reviewers.
Low-code/no-code. Governance teams set up their own forms, questions, routing logic, risk rules and approval paths in the Customization Center, without writing code. Trustible’s Attributes and Rules engine maps intake responses to risk scores, suggested risks and follow-on tasks, so teams can change their questions without rebuilding their risk logic.
“A single AI use case review can take more than six hours, and the queue grows every time a new copilot or agent shows up in the business,” said Gerald Kierce, CEO and Co-Founder of Trustible. “We built Trustible so the people responsible for oversight can set up the governance they need without waiting on engineering, and so AI agents can take on the repetitive parts of intake and triage while people make the calls. We’re thankful to Chartis for the recognition.”
Read the Chartis RiskTech100® 2027 report
Chartis evaluated more than 900 risk technology vendors for this year’s RiskTech100 and introduced new Disruptor categories alongside its rankings. The report covers who’s shaping the market and where AI is changing risk technology.
Why lifecycle governance is important for agentic AI
Chartis singled out agentic AI as a growing source of model and technology risk. Trustible’s position is that agents don’t need a separate governance program. They belong in the same inventory and risk process as every other AI system, with reviews focused on what an agent can reach, what triggers it and whether its actions can be undone. Agents also pick up new tools and connections after they’re approved, so Trustible treats those changes as a reason to reopen the review rather than relying on the original sign-off.
The company holds the AI in its own platform to the same standard. Each Trustible agent has a narrow task, cites its sources, logs every action and leaves the decision to a person. Trustible sees that as the bar enterprises should set for any agent, including the ones that govern AI.
To see how Trustible handles intake, risk scoring and agentic governance, request a demo.