How attack path mapping helps AI security agents prioritize risk
Security teams are deploying AI agents with attack-path mapping to stop threats before they reach sensitive data — not after.
Why it matters
AI agents need better threat context to prioritize risk effectively. Attack path mapping using graph databases is emerging as the infrastructure that lets agents understand how breaches actually propagate, shifting security from alert-chasing to proactive threat interception.
The key facts
10 to knowAttack path mapping uses graph databases to model threat progression across cloud, identity, and device boundaries
Practitioners adopting attack path mapping to give AI security agents actionable prioritization
Security teams historically chased isolated alerts; attackers moved fluidly across infrastructure
Alex Chantavy (co-founder) featured as subject matter expert
Neo4j and graph databases emerging as infrastructure enabling agent decision-making in security
Attack path mapping uses graph databases to show agents how threats could reach sensitive data
Agents can now prioritize which risks to act on first rather than responding to isolated alerts
Use case: security operations automation across cloud, identity, and device boundaries
Source: Alex Chantavy, co-founder (likely Neo4j or related graph/security vendor)
Published July 2026 — deployment trend story
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Publisher excerpt: Security teams have spent years chasing alerts in isolation while attackers move fluidly across cloud, identity and device boundaries. That mismatch is pushing more practitioners toward attack path mapping — using graph databases to show AI agents exactly how a threat could reach sensitive data,…