Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Agent observability tools capture traces, metrics, logs, and evaluations across AI agent workflows. They help teams reconstruct execution paths, inspect tool calls and handoffs, diagnose failures, and monitor latency, cost, and output quality. This guide also covers complementary security platforms that discover agents, enforce runtime policies, or control the privileges agents receive. Agents don’t fail in straight lines.

Pull an access review on almost any Elasticsearch cluster and you’ll find the same thing: roles created for a migration two years ago, analyst accounts with broad read access to indices they queried exactly once, and service accounts nobody can quite explain. None of it was granted carelessly, and all of it is still there. That leftover access is the problem.

Static roles work until the environment changes. Authentication can confirm who or what is making a request, but it cannot determine whether that identity should perform a specific action under current conditions. As infrastructure, services, and AI agents evolve, permissions granted months ago rarely reflect what an identity actually needs to accomplish. Among companies planning to deploy agentic AI within two years, only 21% report having a mature model for agent governance.

Agentic AI security solutions help teams discover, govern, monitor, and control AI agents, copilots, LLM apps, MCP servers, and autonomous workflows. For security and DevOps leaders, they matter because agents can act across production systems. This guide compares leading tools and explains how to choose the right fit. AI agents are moving from assistants to actors.

Identities in the modern enterprise are increasingly less human and more autonomous, powered by the rise in AI agents, APIs and other non-human identities (NHIs). The result is often an entitlement sprawl, where operations take place without human oversight and with privileged access. This non-linear evolution has meant many businesses have had to respond using bolted-on tools, rather than one unified, enterprise-grade platform.

AI agent authentication is the process of verifying that an autonomous agent is the identity it claims to be before it interacts with infrastructure, applications, APIs, or data. Because agents often act on behalf of users, services, or workflows, authentication must be paired with delegated context and downstream authorization controls that determine what the agent is allowed to do, which resource it can access, and how long that access should last.

Over 300 million people rely on Microsoft Teams to get their work done every day. That’s a lot of messages, meetings and “you’re on mute” moments happening every day. Teams makes it easy to collaborate. Too easy, sometimes. Anyone can create a new channel, add users and start sharing files in minutes. That’s great for getting work done, but it’s also how things get out of control. If you manage Teams, you’ve probably seen this happen.