Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Enterprise security teams use Exabeam as a security information and event management (SIEM) platform with user and entity behavior analytics (UEBA) to detect insider threats, lateral movement, privilege escalation, credential compromise, and data exfiltration.

Microsoft Sentinel is Microsoft’s cloud-native SIEM for detecting, investigating, and responding to threats across your environment. To query security data and run analytics rules, Sentinel expects telemetry data in specific table schemas. But firewall, VPN, and network detection logs arrive in vendor-specific formats, making source-specific mappings difficult to maintain as environments grow.

How teams are leaving value on the table and what it costs when they do It's 3:00 a.m. Your on-call engineer gets paged that the central processing unit (CPU) is at 97% on payment-processor-01. They open their observability platform, look at the metric spike, reboot the host, and close the ticket.

Security teams face a flood of noisy logs that arrive in many formats, and often without the context teams need to detect and prioritize threats. But even normalized and contextually enriched log data is only as useful as the speed at which teams can act on it. Any step that requires manual effort can become a bottleneck that makes it difficult to keep pace with the volume.

Security and IT teams are managing growing volumes of logs, alerts, and telemetry data across disconnected tools and data stores. When teams are tasked with responding to audit or compliance requests, they need to pull the right information quickly and accurately and that is rarely a straightforward process. Investigations span multiple tools, logs sit in storage, routing and retrieving archived logs are manual processes that overburden teams when they can least afford it.

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.

Security teams are dealing with a fundamentally different operating environment than they were a few years ago. AI-assisted development is rapidly pushing more code and infrastructure into production, and according to Datadog’s 2026 State of DevSecOps report, 40% of running services have an exploitable vulnerability.

Every guide to AI agent observability tells you what to capture — prompts, tool calls, token usage, traces, syscalls. Almost none address which of those signal sources you can still trust when the agent itself is part of the threat model. That distinction is the entire difference between observability that helps your SRE team debug a slow reasoning chain and observability that helps your security team investigate a breach.

A platform team at a mid-size SaaS company runs three LangChain agents and one AutoGPT-derived planner on EKS. LangSmith is wired in. OpenTelemetry traces flow into their observability stack. Falco runs on every node. The setup is what most security teams would consider thorough. A pip dependency in one of the agents’ tool packages ships a malicious update.

In August 2025, a vulnerability chain in NVIDIA Triton Inference Server was found that allowed an unauthenticated remote attacker to send a single crafted inference request, leak the name of an internal shared memory region, register that region for subsequent requests, gain read-write primitives into the Triton Python backend’s private memory, and achieve full remote code execution. The exploit chain ran entirely through Triton’s standard inference API. No anomalous traffic volume.