Don't Build AI Governance From Scratch. Just Turn It On.

Organizations are deploying AI agents at scale, often without centralized oversight, creating risks such as data exposure and regulatory scrutiny. Gartner estimates that by 2028, Fortune 500 enterprises will average 150,000 agents, yet only 13% believe they have adequate governance. Atlassian’s solution embeds governance into its existing platform, leveraging tools like Rovo to operate within familiar permissions and administrative controls, reducing the need for separate governance layers.

Security teams face challenges in tracking AI agents’ access, creation, and actions, often leading to adoption slowdowns or ungoverned workarounds. Atlassian’s approach integrates AI governance into the Teamwork Graph, where projects, code, and policies already reside. This ensures agents operate within established boundaries, with layered controls like scoped permissions and tool restrictions to prevent unauthorized data access or workflow execution.

Data security remains the foundation of AI governance. Atlassian Guard provides tools to classify, monitor, and secure data before AI interactions occur, preventing scenarios like unauthorized code or compensation data exposure. Additional protections include real-time scanning of third-party connectors and user-level data to block sensitive content from entering prompts or responses, supporting compliance with encryption and data residency requirements.

As AI agents become more autonomous, granular controls and operational visibility are essential. Atlassian’s governance model includes records, reporting, and continuous monitoring to track agent actions, ensure policy adherence, and maintain compliance. By integrating governance into existing systems, organizations can scale AI adoption without sacrificing security or oversight, addressing accountability demands without impeding progress.