The AI security agent promise: autonomous investigation, instant triage, machine-speed response. The reality? AI agents hallucinate, generate false positives, and make low-confidence decisions because they lack the one thing human analysts have—infrastructure context.
Today, we're excited to introduce the Infrastructure Intelligence Graph—the knowledge layer that transforms AI security agents from unreliable assistants into autonomous decision engines.
The Agentic SOC Problem: Context Starvation
Every vendor is racing to build AI-powered SOC agents. Autonomous investigation. Intelligent triage. Self-healing security operations.
But here's what no one talks about: AI agents are only as good as the context they can access.
Give an AI agent a suspicious IP with no context, and it does what LLMs do—it guesses:
- "This IP has medium risk based on reputation data"
- "Recommend monitoring for 24 hours"
- "Possibly related to scanning activity"
These aren't decisions. They're hallucinations wrapped in confidence scores.
Why AI Agents Fail at Security Operations
Fragmented Intelligence: Threat feeds, SIEM logs, EDR detections scattered across tools. AI agents can't synthesize what they can't access.
"No Data" for 95% of Indicators: Unknown IP → Generic reputation lookup → "No significant data." AI agents make poor decisions because they're operating blind.
No Infrastructure Lineage: Is this IP a scanner? VPN exit node? Bulletproof hosting? Adversary C2? Without lineage, AI agents can't distinguish signal from noise.
No Temporal Context: When was this infrastructure created? What changed in the last 48 hours? AI agents treat every alert as isolated, missing the campaign narrative.
What AI Agents Actually Need: A Living Knowledge Graph
Human analysts are effective because they understand infrastructure context. They know:
- What something IS (scanner vs. VPN vs. adversary infrastructure)
- How it CONNECTS (IP → domain → cert → campaign)
- How it BEHAVES (temporal patterns, infrastructure churn)
- How it RELATES to your environment (targeting YOU vs. broader internet)
AI agents need the same context—at machine scale.
The Infrastructure Intelligence Graph: Context at Machine Scale
The Infrastructure Intelligence Graph is a continuously updated, multi-source knowledge layer that gives AI agents what they need to operate autonomously.
1. Complete Infrastructure Classification
Not this (traditional threat intel):
- IP: 203.0.113.42 | Risk Score: 7.2/10 | Category: Unknown | Recommendation: Monitor
But this (Infrastructure Intelligence Graph):
- Classification: Bulletproof hosting, known for credential harvesting infrastructure
- Relationships: 47 domains, 23 related IPs, 5 certificates (shared pattern)
- Hosting: AS64512 (ShieldHost Ltd), historically associated with phishing
- Anonymization: Behind residential proxy network (identified)
- Temporal: Registered 12 days ago, cert changed 48 hours ago
- Your Environment: Probed your SSO endpoint 3x last week, matches EDR anomaly pattern
- Campaign: Active credential harvesting operation targeting financial services
- Confidence: 94% | Recommendation: High-confidence block with campaign-wide enforcement
AI agents can now make informed decisions instead of educated guesses.
2. Infrastructure Relationships = Campaign Understanding
The graph maps complete adversary operations:
IP ↔ Domain ↔ Certificate ↔ ASN ↔ Hosting ↔ Services ↔ Temporal Patterns
One alert becomes campaign-level intelligence: "This suspicious IP is part of a 47-domain phishing operation that's been building for 12 days and accelerated 48 hours ago."
3. Multi-Source Fusion = High-Confidence Decisions
The graph fuses external intelligence (Pulse Sensors™ across 600M+ datacenter IPs, domain intelligence, TLS/PKI monitoring, honeypot networks, anonymization detection) with internal signals (your SIEM logs, EDR detections, network traffic, attack surface exposure, incident history).
Result: AI agents operate with both global threat intelligence AND local environment context—just like elite analysts do.
4. Temporal Intelligence = Predictive Action
The graph tracks infrastructure over time: build-out phase (2-8 weeks before weaponization), staging activity, pre-weaponization signals, active operations, and rotation patterns.
AI agents can now predict threats, not just react to them.
5. Evidence Trails = Explainable Automation
Every graph relationship includes reason codes (why entities are connected), confidence scores (strength of relationship), supporting signals (what evidence backs this), and temporal context (when relationships formed).
AI agents can explain their decisions—critical for automation teams can trust.
The Agentic SOC in Action: Before and After
Scenario: Unknown Destination Alert
WITHOUT Infrastructure Intelligence Graph:
AI Agent Log:
[09:23:15] Alert received: Suspicious outbound connection
[09:23:16] IP: 203.0.113.42
[09:23:18] Querying threat intelligence feeds...
[09:23:22] Result: No significant threat data found
[09:23:26] Recommendation: Medium risk, monitor for 24 hours
[09:23:27] Action: Create monitoring ticket
Analyst Time Required: 2-4 hours for manual investigation
Outcome: Generic "monitor" recommendation, no context
Risk: Potential C2 traffic continues while under monitoring
WITH Infrastructure Intelligence Graph:
AI Agent Log:
[09:23:15] Alert received: Suspicious outbound connection
[09:23:16] IP: 203.0.113.42
[09:23:17] Querying Infrastructure Intelligence Graph...
[09:23:18] Graph Intelligence Retrieved:
CLASSIFICATION: Bulletproof hosting, credential harvesting infrastructure (94% confidence)
CAMPAIGN: 47 domains, 23 IPs, 5 certificates—12-day build-out, accelerated 48hrs ago
INTERNAL CORRELATION: Probed your SSO 3x in 7 days, matches EDR anomaly on 2 endpoints
RISK: 9.2/10—High confidence, active campaign, internal targeting
[09:23:19] AI Agent Decision: High-confidence threat, autonomous containment authorized
[09:23:20] Actions Initiated:
✓ Isolating source endpoint
✓ Blocking 47 domains, 23 IPs at perimeter
✓ Creating SIEM correlation rule for cert pattern
✓ Scanning 8 potentially affected endpoints
[09:23:24] Decision Sync: Policy propagated across stack
✓ Firewall: 24 entities blocked
✓ DNS: 47 domains sinkholed
✓ EDR: Process execution blocked on 8 endpoints
✓ SIEM: Correlation rules deployed
[09:23:28] Outcome: Campaign disrupted—Mean Time to Contain: 13 seconds
[09:24:00] Analyst Notification: "Campaign automatically contained.
Review summary and approve continued monitoring for rotation."
Analyst Time Required: 2 minutes for review
Outcome: Complete campaign disruption with explainable decisions
Risk: Zero C2 contact, proactive containment
What This Enables: Truly Autonomous Security Operations
Machine-Speed Triage with Human-Grade Confidence
AI agents make decisions in seconds that would take analysts hours. From alert to containment: 13 seconds. Not 2-4 hours.
Autonomous Investigation That Actually Works
AI agents pivot from one IOC to complete campaign, correlate external intelligence with internal detections, generate hunt queries, and produce evidence-backed summaries. No hallucinations. Context-driven conclusions.
Explainable Automation Leadership Trusts
Every decision includes classification with reason codes, confidence scores with supporting evidence, campaign relationships, temporal context, and internal correlation. Security leadership can audit, defend, and trust autonomous decisions.
Pre-Attack Operations at Scale
AI agents monitor global infrastructure formation 24/7, detect staging activity 2-8 weeks before weaponization, identify infrastructure churn, and automatically enforce blocks during build-out phase. From reactive detection to predictive prevention.
Self-Improving Intelligence
The graph learns from every investigation. AI agents feed findings back, strengthening campaign clustering, improving predictive models, and enhancing environment-specific intelligence.
What This Means for Your Security Team
For SOC Teams: AI agents handle 70-90% of tier 1 triage autonomously. MTTR reduced from hours to seconds. Scale protection without scaling headcount.
For Detection Engineering: AI agents generate detection rules from campaign patterns with continuous improvement and proactive coverage of emerging threats.
For Incident Response: Autonomous containment for high-confidence threats with complete campaign context available instantly. Proactive disruption before incidents occur.
For Security Leadership: Demonstrable ROI from autonomous operations with audit-ready decision trails and measurable risk reduction through pre-attack intelligence.
The Future is Agentic. The Foundation is the Graph.
AI security agents are inevitable. But without infrastructure intelligence, they're unreliable.
The Infrastructure Intelligence Graph transforms AI agents from unpredictable assistants into autonomous decision engines operating with complete infrastructure context, campaign-level intelligence, evidence-backed conclusions, machine-speed execution, and human-grade confidence.
This is the foundation for truly autonomous security operations.
Want to see the Agentic SOC in action?
We'll show you how AI agents with infrastructure intelligence operate autonomously—detecting, investigating, and containing threats in seconds, not hours.
Book a Demo and watch an AI agent disrupt a live campaign with complete explainability.
Stop hoping AI agents work. Give them the intelligence layer they need.
The Infrastructure Intelligence Graph: The knowledge layer for autonomous security operations.
VisionHeight: Intel that turns unknown infra into autonomous decisions.
Heading 1
Heading 2
Heading 3
Heading 4
Heading 5
Heading 6
Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
Block quote
Ordered list
- Item 1
- Item 2
- Item 3
Unordered list
- Item A
- Item B
- Item C
Bold text
Emphasis
Superscript
Subscript