Best AI Agent Security Platforms for Confluence

Compare the Top AI Agent Security Platforms that integrate with Confluence as of November 2026

This a list of AI Agent Security platforms that integrate with Confluence. Use the filters on the left to add additional filters for products that have integrations with Confluence. View the products that work with Confluence in the table below.

What are AI Agent Security Platforms for Confluence?

AI agent security platforms help organizations secure, monitor, govern, and control autonomous AI agents operating across enterprise systems, applications, and data environments. These platforms provide capabilities such as identity management, permission controls, policy enforcement, activity monitoring, threat detection, and audit logging to ensure AI agents act within approved boundaries. They help protect against risks such as unauthorized actions, prompt injection attacks, data leakage, tool misuse, and malicious agent behavior. Many AI agent security platforms integrate with AI orchestration frameworks, IAM systems, security operations tools, and compliance platforms to provide end-to-end governance and protection. By enabling secure deployment and oversight of AI agents, these platforms help organizations scale agentic AI adoption while maintaining security, compliance, and operational trust. Compare and read user reviews of the best AI Agent Security platforms for Confluence currently available using the table below. This list is updated regularly.

  • 1
    CloudEagle.ai

    CloudEagle.ai

    CloudEagle.ai

    CloudEagle.ai is an AI-powered SaaS Management, AI Governance, and SaaS Security platform that gives enterprises one command center to discover, secure, govern, and optimize their entire SaaS and AI stack, including AI usage control and both human and non-human identities. CloudEagle governs applications, identities, and non-human identities (service accounts, bots, API keys, AI agents) continuously, rationalizing redundant tools and tracking security posture against frameworks like NIST 800. Onboarding takes 30 minutes; savings and governance start day one. To date: $50B+ in spend processed, $3B+ saved. With 500+ integrations, CloudEagle eliminates shadow SaaS and AI, enforces AI usage policies, and secures non-human identities. Slack-enabled, no-code workflows automate onboarding/offboarding, access reviews, license harvesting, and just-in-time access, while an autonomous agent triggers renewals and routes approvals through Slack, Jira, or ServiceNow.
    Starting Price: $2000/month
  • 2
    Willow

    Willow

    Willow

    Willow is a leading enterprise platform for governing AI agents, tools, MCP servers, skills, and plugins. It connects agents such as Claude, ChatGPT, Cursor, Codex, Gemini, n8n, and custom agents to internal systems and SaaS tools, including Jira, GitHub, Slack, and databases, through an MCP gateway. Agents receive human-linked identities, scoped permissions, and audit trails. Security teams define policies, while employees self-serve approved connectors, skills, and plugins. Features include shadow AI discovery; PII redaction and prompt-injection inspection; app- and action-level permissions; SSO, SCIM, and RBAC with Okta, Entra ID, and JumpCloud; SIEM export; token and cost monitoring; and background agents. Willow is available as SaaS, dedicated cloud, or on-premises in a customer VPC. It holds SOC 2 Type II attestation, is GDPR-ready.
    Starting Price: $0
  • 3
    Pi

    Pi

    Pi Security

    Pi is an agentic product security platform that builds institutional security memory so organizations can find, fix, and prevent recurring vulnerabilities without slowing development. It continuously ingests codebases, past incidents, pentest reports, tickets, and other security history into a living inventory, giving the system context about how the organization builds and secures software. When a vulnerability is found, Pi traces it to its architectural root cause, searches for variants across the codebase, and helps close the entire class of issue rather than treating each finding independently. Remediation is generated in the context of the organization’s languages, architecture, and conventions, then delivered directly into developer workflows. What the system learns becomes prevention guardrails that can be applied in IDEs and pull requests during design and coding, blocking known insecure patterns before they reach production.
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