AI SIEM
A SIEM where the AI does the work, not the configuration
Natural language query, AI-generated detection rules with backtest validation, and LLM-assisted incident summaries — all built into the core SIEM layer. No AI add-on license required.
Natural language query
Ask your security data questions in plain English. ManySignal compiles your question into a structured query, shows you the compiled SQL-like syntax, and returns results — with entity context attached to each row.
"Show me all logins from outside normal locations for finance users last 7 days"
"Which service accounts accessed more than 100 resources in the last hour?"
"Find all PowerShell executions with base64-encoded commands this week"
"List all S3 buckets that became public in the last 30 days"
Compiled query output
The natural language query compiles to a structured ManySignal query you can review, edit, and save. Full transparency — you see exactly what ran.
-- ManySignal AI SIEM — Natural Language Query
-- Input: "Show me all logins from outside normal locations for finance users last 7 days"
-- Compiled query (editable):
SELECT
u.display_name,
u.department,
e.source_ip,
geo_lookup(e.source_ip) AS location,
e.timestamp,
u.usual_countries AS expected_locations,
u.risk_score
FROM identity.auth_events e
JOIN entity_graph.users u ON e.user_id = u.id
WHERE
u.department = 'Finance'
AND e.timestamp >= NOW() - INTERVAL 7 DAYS
AND geo_lookup(e.source_ip).country
NOT IN (u.usual_countries)
AND e.outcome = 'success'
ORDER BY u.risk_score DESC, e.timestamp DESCAI SIEM capabilities
Natural language query across 12 months
Ask questions in plain English. ManySignal compiles to structured query, executes, and returns entity-annotated results.
AI-generated detection rules with backtesting
Describe a threat scenario. ManySignal generates a detection rule and tests it against 90 days of your telemetry before deployment.
LLM-assisted incident investigation
The Investigate agent generates a narrative investigation summary in addition to the structured case timeline.
Automated post-incident reports
Case close triggers an LLM-generated post-incident report: root cause, timeline, actions, and hardening recommendations.
Anomaly detection without rule configuration
ML models identify behavioral anomalies per entity automatically — no anomaly detection rules to write.
AI confidence scores on every detection
Each alert has a 0–100 confidence score from the triage agent, reducing analyst time spent evaluating low-confidence findings.
AI SIEM — common questions
What does 'AI-native' mean for a SIEM?
AI-native means AI is built into the core detection, investigation, and query pipeline — not added as an optional module. In ManySignal, every alert is triaged by AI before a human sees it, every investigation assembles evidence automatically, and the query interface accepts natural language in addition to the structured query language. AI is not a premium add-on; it's the default operating mode.
How does natural language query work in practice?
You type a question in plain English — 'Show me all admin logins outside business hours in the last 30 days' — and ManySignal compiles it into a structured query against your telemetry. The compiled query is visible and editable before execution. You can use natural language as a starting point and refine the generated query manually.
Does AI-generated detection logic require human review?
Yes. AI-generated detection rules include a backtest report showing how they would have fired against 90 days of historical telemetry. A human analyst reviews and approves the rule before it deploys to production. The AI generates the logic; the analyst owns the decision to deploy it.
How does ManySignal use LLMs in the SIEM context?
LLMs power: natural language query translation, detection rule generation (given a technique description), incident summary generation, and post-incident report drafting. The LLM layer never has autonomous access to containment actions — it operates in read and generate mode only. All actions are governed by the approval-ladder policies your team configures.
What data stays in my environment vs. what goes to ManySignal's AI systems?
Telemetry data is processed and stored in your ManySignal cloud tenancy (US, EU, or APAC region, your choice). LLM-based features send sanitized, non-PII event summaries to the model inference layer — not raw log data. Full data residency documentation is available in the security and compliance package.
Ask your first natural language query in the demo
30-minute session. You describe a threat scenario; we'll show you the AI-generated detection rule, backtest report, and compiled query — against your telemetry if you connect a source first.