Enterprise XDR Agent
Lightweight, enterprise-grade XDR agent for Windows endpoints delivering real-time threat detection, response, and system visibility at scale without dragging down laptops.
What we were solving
Financial services organisation needing endpoint coverage that works offline, respects CPU budgets, and feeds a central SIEM without duplicate agents.
- Legacy AV missed lateral movement and script-based attacks.
- Security team could not afford double agents (EDR + log forwarders) on every laptop.
- Playbooks required safe, reversible remediation - not reboot loops.
What we built
- Built a minimal agent surface focused on high-signal events: process ancestry, network edges, and persistence changes.
- Added policy-driven response: isolate host, kill process tree, or snapshot for IR before rollback.
- Normalized events to a schema the SIEM already understands to avoid custom parsers per customer.
Architecture notes for your engineers
- Ring-buffered telemetry so burst periods do not drop evidence on low-memory endpoints.
Key results
- Single lightweight agent for endpoint visibility and response hooks
- Policy-driven containment with audit-friendly rollback paths
- Schema-stable telemetry mapped to the existing SIEM parsers
- Deployment model sized for large laptop fleets without duplicate agents
What it was built on
Representative tools and patterns — exact vendors vary per client environment.
Endpoint
Backend
Integrations
What we'd tell the next team
- Endpoint agents fail in the field on battery and CPU - profile before you ship fancy ML loops.
- Autonomous actions need human-readable audit logs for compliance and trust.
- Prefer schema-stable telemetry over dumping raw logs that SIEMs cannot retain.
Questions this engagement anticipated
Is response fully autonomous?
Policies define what can run automatically versus what requires human approval; every automated action is logged for SOC and compliance review.
Why optimise for SIEM schema first?
Because retention and parser cost decide whether telemetry is actually usable in investigations - raw noise becomes expensive quickly at enterprise scale.
Go deeper on the thinking
Compare your situation to this case.
Bring your constraints - environment, timeline, and budget. We scope before we quote.