Anvilogic 8.0 is generally available

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The Agentic SOC

Your SOC never stops, now it never sleeps.

Agents can only automate what they can see. Anvilogic sits above every SIEM and data lake and puts agent teams to work across the whole lifecycle: intel, data, detection, hunting, triage, and investigation.

It all runs continuously on one Enterprise Security Graph, with humans in the loop exactly where you want them.

Book a Demo → See the agent lifecycle →

Agent Activity RUNNING 24/7

• 02:14 Intel Agent: Scattered Spider shift, 3 priorities re-ranked ✓ DONE
• 02:31 Data Agent: Okta feed latency 46 min, gap flagged for identity ✓ DONE
• 03:07 Detection Agent: 2 coverage gaps closed, detections deployed ✓ DONE
• 04:52 Triage Agent: 148 alerts prioritized, 121 benign to tuning ✓ DONE
• 05:19 Investigation Agent: CASE escalated, containment recommended

AWAITING YOUR APPROVAL IR on-call notified

Every action written back to the Enterprise Security Graph

CONTINUOUS

24/7

Agents working the lifecycle without a shift change

AGNOSTIC

Any SIEM, data lake, or hybrid of both, with no migration required

ORCHESTRATION

Multi-agent Teams of agents across intel, data, detection, hunt, triage, and response

FOUNDATION

1 Enterprise Security Graph with shared context and memory

The Whole SOC Lifecycle, Always Running

Not a chain of point tools handing off tickets. Teams of agents operating on the same graph, each one feeding the next, and feeding back.

NOW RUNNING:

• Data agents scanning feed health and visibility

1. WORKING Intel & Business Context
   • Threat landscape and business threat models set the priorities.
2. WORKING Data Health & Visibility
   • Every connected environment scanned for health and blind spots.
3. WORKING Detection & Hunting
   • Gaps filled, hunts run, detection health kept honest.
4. WORKING Triage & Verdicts
   • Alerts prioritized and given an initial verdict in seconds.
5. WORKING Cases & Investigation
   • End-to-end analysis, incident calls, remediation.

FEEDBACK · VERDICTS AND OUTCOMES RETRAIN THE LOOP

Enterprise Security Graph

• entities
• relationships
• detections
• verdicts
• analyst memory

Shared context for every agent · improves with every action

Learn more about the AI Operating System →

Threat Priorities FINANCIAL SERVICES re-ranked 2h ago

61 MATURITY

• Identity Compromise 88%
• Payment Fraud Abuse 64%
• Third-Party Access 37%

Intel Agent

Scattered Spider adding help-desk MFA reset abuse. T1621 promoted to Tier 1.

Business Threat Model

Imported: 14 crown-jewel systems, 3 regulated data domains, 2 M&A entities.

Priorities recomputed continuously, so coverage targets follow the threat, not the calendar.

Intel & Business Context

Always know what matters, inside and out.

Automation without priorities is just faster busywork. Intel agents watch the threat landscape around the clock and re-rank your priorities as adversary behavior shifts, while your own business threat models (crown jewels, regulated data, acquisitions, third parties) are imported directly into maturity scoring. The result is one live picture of what you should be defending, and how well you actually are.

• → Intel agents monitor adversary TTP changes and adjust threat priorities automatically
• → Import business threat models so priorities reflect your actual risk, not a generic list
• → Maturity scoring keeps coverage measured against those priorities continuously

Data Agents

Agents watch your data before detections break.

Data agents continuously scan every connected environment (SIEM, data lake, cloud, identity, endpoint) for feed health, schema drift, and volume anomalies. They also work the other direction: when a threat priority needs telemetry you aren't collecting, the agent tells you exactly which source to onboard and what detection controls it unlocks.

• → Continuous health checks on every feed across every connected platform
• → Visibility gap recommendations tied to the detections they would enable
• → Schema drift and latency caught before detections silently stop firing

Feed Health · 34 connected sources scanned 6 min ago

• Splunk · Windows Event Logs 1.4 TB/day HEALTHY
• Snowflake · EDR Process Events 3.8 TB/day HEALTHY
• Okta · System Log 46 min lag LATENCY

Visibility Gap

No AWS CloudTrail management events in scope. Onboarding unlocks 37 detections across 9 Tier 1 techniques.

Coverage Work Queue driven by Tier 1 priorities

• Gap closed · MFA Reset Abuse DEPLOYED T1621 · AVL_R100007412 → Snowflake, Sentinel
• Hunt running · Third-Party Access Anomalies 24/7
  • 18 of 25 hypotheses tested · 2 leads escalated
  • Tuning insight · AVL_UC1116−23% NOISE
  • Allowlist candidate identified from 936 benign events.
  • Health agent · AVL_UC1035 failed to run FIX READY
  • Root cause explained, corrected logic staged for approval.

Detection, Hunt & Health Agents

Coverage that closes its own gaps.

Detection and hunt agents inherit the context from intel and your business threat models, then go to work: finding where coverage is missing, building and deploying the detections to close it, and hunting proactively for what no rule caught. Health and tuning agents run alongside them, confirming detections still execute, still get data, and still fire for the right reasons.

• → Gap analysis and detection creation driven by your live threat priorities
• → Proactive hunts running 24/7 against every connected environment
• → Health and tuning agents keep deployed detections working and quiet

Triage Agents

Every alert prioritized, verdicts on arrival.

Triage agents pull entity, asset, and historical context from the security graph, rank what actually deserves attention, and assign an initial verdict with its reasoning attached. Anything marked benign doesn't just disappear. It flows straight back to the tuning agents, so the detection that produced it gets quieter in real time.

• → Graph-aware prioritization using asset criticality and prior verdicts
• → Initial verdicts with transparent reasoning an analyst can audit
• → Benign verdicts loop back into tuning to remove noise at the source

Triage Queue · last hour 148 alerts in

• 7 Escalated to case
• 20 Needs analyst review
• 121 Benign verdict

Impossible travel · svc-payments-api MALICIOUS · 0.91

Service account on a crown-jewel system, no prior geo, matches Tier 1 priority. Escalated to CASE-4471.

Encoded PowerShell · build-agent-14 BENIGN · 0.96

Known CI job, 412 identical prior verdicts in the graph. Sent to tuning.

BENIGN → TUNING AGENT · DETECTION UPDATED IN REAL TIME

CASE-4471 Payments API credential abuse INCIDENT

Scope expanded from the graph

3 related alerts, 2 hosts, 1 identity, 1 SaaS app linked to the same entity cluster.

Evidence collected across platforms

Queries run against Splunk, Snowflake, and Sentinel with no analyst pivoting required.

Verdict: incident declared

Valid credentials abused from unmanaged infrastructure; regulated data in scope.

Remediation recommended

Revoke token, force re-auth, isolate build-agent-22, block egress IP.

Human in the loop: containment awaiting approval from IR on-call

Approve

Cases & Investigation Agents

End-to-end investigation, not just a summary.

Escalations become cases, and investigation agents work them from first pivot to final call: expanding scope through the security graph, gathering evidence across every connected platform, deciding whether this is an incident, and recommending the remediation, or enforcing it once you approve. The whole chain of reasoning stays in the case for review, audit, and handover.

• → Case management for every escalation, with full investigative history
• → Cross-platform evidence gathering without analyst tool-hopping
• → Incident decisions and remediation, recommended or enforced once you approve

Enterprise Security Graph

Context and memory make agents trustworthy

Every agent reads from and writes to the same Enterprise Security Graph: your entities and their relationships, your threat priorities, your detections and their history, and every verdict and investigation your team has ever reached. That's the difference between an agent that guesses and one that remembers, and it's why the loop gets measurably better the longer it runs.

• → Shared context across every agent, with no re-deriving the environment each run
• → Persistent memory of past verdicts, tuning decisions, and investigations
• → Runs on your data where it already lives, across any SIEM or data lake

Graph Layers

• ENTITIES: Users, hosts, identities, services, crown-jewel systems
• BEHAVIOR: Techniques observed, detections fired, hunts run
• MEMORY: Verdicts, tuning decisions, closed investigations, analyst intent

Compounding Effect

• Day 1
• Month 1
• Month 3
• Month 6

Autonomy grows as the graph learns your environment: more work handled end to end, fewer escalations that need a human.

Humans in the Loop

Autonomy you own, step by step

Agentic doesn't mean unsupervised. Set the level of autonomy per stage, and move it as trust builds.

• Notify: Agents do the work and report it. You review the reasoning, nothing changes without you.
• Approve: MOST COMMON Agents stage detections, tuning changes, and containment. A human clicks approve.
• Delegate: For the work you've watched enough times, agents execute end to end and log it all.

Get Started

See the Agentic SOC running on your workflows.

Keep the SIEM you have, keep the lake you're building. We'll show you the loop running against your environment in a 30-minute session.

Book a Demo →