Security Information and Event Management (SIEM)
Security Information and Event Management (SIEM) aggregates and correlates logs across the IT estate for real-time alerting, investigation, and compliance.
What a SIEM does
A SIEM unifies two older disciplines: Security Information Management (long-term log storage and reporting) and Security Event Management (real-time monitoring and alerting). Combined, it gives a SOC a single place to detect, investigate, and document security activity across an otherwise fragmented estate.
The core value is correlation. Any one log source — a firewall deny, a failed login, a new process on an endpoint — is rarely conclusive on its own. A SIEM joins these signals across sources and time so that a sequence of individually unremarkable events (a successful login from a new country, followed by a privilege escalation, followed by a bulk data read) becomes a single, prioritized alert.
A SIEM also serves the compliance function. Regulations and frameworks — PCI DSS, HIPAA, SOC 2, ISO 27001, DORA — require organizations to retain security-relevant logs, monitor for specific events, and produce evidence on demand. The SIEM is typically the system of record that satisfies these obligations.
Core components of a SIEM
A complete SIEM deployment is built from several stages that data passes through, from ingestion to alert.
Log collection and aggregation
Agents, forwarders, and API integrations pull events from across the environment: syslog from network gear, Windows Event Logs and EDR telemetry from endpoints, audit logs from identity providers, flow logs from cloud platforms, and application logs. The breadth of this collection defines what the SIEM can ever detect — a source that is not ingested is invisible to the SOC.
Normalization and parsing
Raw logs arrive in dozens of incompatible formats. The SIEM parses each source and maps its fields to a common schema — user, host, source IP, action, outcome, timestamp — so that events from different vendors can be queried and correlated together. Without normalization, cross-source correlation is impossible.
Correlation rules and detection
Detection logic runs over the normalized stream. Rules range from simple thresholds (five failed logins in a minute) to multi-stage correlation searches mapped to frameworks like MITRE ATT&CK. When conditions match, the SIEM raises an alert with the supporting events attached.
Alerting, dashboards, and retention
Alerts flow to analysts through dashboards, queues, and notifications. Retained event history — often months to years, depending on compliance requirements — supports investigation, threat hunting, and audits long after the original activity occurred.
SOAR and UEBA pairings
Modern SIEMs are frequently paired with SOAR (Security Orchestration, Automation and Response) to automate triage and containment, and UEBA (User and Entity Behavior Analytics) to baseline normal behavior and flag statistical anomalies that static rules miss.
The AI visibility gap
A SIEM can only correlate what it ingests, and it can only ingest events that some system actually emits. This is precisely where AI activity falls outside its reach.
When an employee pastes a confidential document into a chatbot, when a model returns regulated data drawn from a connected system, or when an autonomous agent reads an internal record and calls an external API, no SIEM-ingestible event is generated. The prompt is free-form text inside an encrypted session to a third-party provider. The completion never crosses an inspectable boundary. The agent's tool call executes through an API the SIEM has no integration with. From the SOC's perspective, the most consequential AI actions simply did not happen.
The table below contrasts what a SIEM sees against the AI-layer activity it cannot.
| SIEM event view | AI-activity audit view | |
|---|---|---|
| Data source | Network, endpoint, identity, app logs | Prompts, completions, and agent tool calls |
| Visibility | Connections, processes, authentications | Intent and content of each AI interaction |
| AI prompts | Encrypted session to a provider, no content | Full prompt text, with policy decision recorded |
| Model completions | Not observed | Output inspected before it reaches the user |
| Agent tool calls | None — calls never traverse an ingested channel | Each tool call and argument captured before execution |
| Audit unit | Log line per device or host event | Trace-linked record per prompt, completion, and tool call |
The gap is not a flaw in SIEM design. It is that AI introduced a new, high-volume activity channel that produces no native telemetry — so unless a control at the AI layer emits events, there is nothing for the SIEM to collect, correlate, or retain.
Closing the gap without replacing the SIEM
The fix is not to abandon the SIEM but to feed it the signal it is missing. A control positioned at the AI interaction layer can observe prompts, completions, and agent tool calls directly, then emit them as structured, normalized events the SIEM can ingest like any other source.
Once AI activity reaches the SIEM as first-class events, everything the SOC already does extends to it: correlation rules can join an AI data-exfiltration attempt with the same user's endpoint and identity activity, dashboards gain AI usage visibility, and retained AI audit records satisfy the same compliance obligations as the rest of the estate. The SIEM remains the system of record; the AI layer supplies the events it cannot generate on its own.
