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Introducing HaxUnit: Continuous visibility for your external attack surface

Author HaxUnit Team · · 7 min read
HaxUnit overview

Security programs wrestle with a simple reality: the internet changes faster than most inventories. New assets appear, ownership drifts, and signals blur. HaxUnit gives teams a continuous, accurate picture of their external attack surface — and the workflows to act on it.

Why we built HaxUnit

Most exposure tools either flood teams with unactionable alerts or miss context that matters. We’re building a system that focuses on signal quality, asset ownership, and operational fit with modern engineering teams.

What HaxUnit does

  • Automated discovery across domains, subdomains, IPs, cloud edges, and services.
  • Contextual risk that blends exposure, exploitability, blast radius, and business ownership.
  • Unified exposure graph that preserves relationships and change over time.
  • Developer-friendly workflows that route issues to the right owners with clear remediation.
  • Noise controls including deduplication, rate limiting, suppressions, and SLAs.
  • Integrations with ticketing, chat, and CI to meet teams where they work.

How it works

Under the hood, a streaming pipeline populates an exposure graph. Ingested observations are normalized, enriched, and upserted. Changes trigger detections that are deduplicated and routed with ownership and severity context.

// pseudo
discover()
  .normalize()
  .enrich()
  .graph_upsert()
  .detect_changes()
  .route_to_workflows()

Who it’s for

Security and platform teams that want continuous coverage without alert fatigue. Whether you’re mapping a new environment or taming an existing surface, our graph and workflows adapt to your operating model.

What’s next

Over the next few months we’ll ship deeper service fingerprinting, attack-path visualizations, and tighter integrations. We’re also investing in ownership inference and risk tuning for large, distributed orgs.

Want to try it or share feedback? Request a demo — we’d love to hear from you.

Up next

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