Stop public IaaS breaches

Challenges with IaaS threats
Exploding log volume
Cloud environments generate millions of logs daily, creating a data avalanche that buries critical security signals in noise, making manual analysis impossible and storage costs unsustainable.
Alert overload
Routine admin tasks and everyday engineer activity across public clouds trigger a flood of alerts, masking the few that truly matter.
Coverage blindspots
Development teams leverage new IaaS services faster than rules and traditional anomaly detection can keep pace, continuously creating coverage gaps.
Investigation complexity
Investigating a cloud attack requires reconstructing the attack path from scattered logs, identities, and configurations, demanding deep cloud expertise from talent that's already in short supply.
How Exaforce empowers your cloud SOC
Always-on AI that empowers your SOC or our managed service to detect real attacks, accelerate investigations, and stop breaches.
Exaforce cuts through cloud volume, leaving only real threats with full investigations.


Handles cloud scale
Exaforce intelligently deduplicates and transforms cloud data to slash storage costs while surfacing exactly what Exabots and analysts need for rapid triage, investigation, and response.


Reduced false-positive alerts
Cuts through the flood of alerts from everyday cloud activity, automatically dismissing false positives while escalating real threats that matter.


Detects what others miss
By stitching identities, events, and resource configurations (workloads, repositories, and more), across AWS, GCP, GitHub, and more, Exaforce uncovers blended attack paths that siloed rules overlook.


Simplify investigations
Exabots automatically reconstruct attack paths from scattered logs, identities, and configurations, delivering the deep cloud expertise your team needs without requiring specialized talent.
Frequently asked questions
Investigating cloud attacks traditionally requires specialized knowledge of IAM policies, VPC networking, container orchestration, and cloud-specific APIs across AWS, GCP, and Azure. Exabots eliminate this requirement by automatically reconstructing attack paths from scattered logs, identities, and configurations, correlating which identity performed what action on which resource and why it matters, resolving effective permissions across complex policy hierarchies, mapping lateral movement paths through assumed roles and cross-account access, and presenting findings with complete context in natural language. Junior analysts receive investigated findings with clear evidence and recommendations rather than raw CloudTrail logs requiring expert interpretation. This democratizes cloud security expertise across the entire SOC team.
Traditional cloud security tools rely on single-source detection rules that miss multi-stage attacks spanning identities, resources, and configurations. Exaforce detects blended attack paths like credential theft via phishing followed by console login from attacker infrastructure, privilege escalation through policy modifications that appear benign individually, lateral movement between AWS accounts using assumed roles, data exfiltration disguised as routine S3 operations, and persistence mechanisms established through Lambda functions or modified security groups. The Semantic Model correlates these activities across AWS, GCP, Azure, GitHub, and identity providers to reconstruct complete attack chains that siloed tools evaluating logs or configurations independently cannot identify.
Cloud environments generate millions of events daily, making traditional SIEM storage prohibitively expensive. Exaforce applies security-driven optimization that intelligently deduplicates truly redundant events while preserving subtle variations critical for detection, reduces baseline noise while retaining anomalous signals even at low volume, and uses context-aware filtering to preserve critical low-frequency events like privilege escalation attempts. The platform then transforms and normalizes remaining data for rapid analysis. This approach delivers 60-80% cost reduction compared to ingesting raw logs into traditional SIEMs while actually expanding detection coverage, as teams can now afford to monitor more IaaS services that were previously cost-prohibitive.
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