Your SIEM Sees Fragments
Hunters Need the Whole Path
Threat hunting is the proactive search for adversary activity that has already evaded automated defenses. Anomali unifies network flow, enriched endpoint data, and identity events into one schema, so hunters trace east-west movement across the whole environment instead of manually pivoting between tools that were never built to talk to each other.
Key Takeaways
Attackers move laterally across endpoint, network, and identity boundaries, but most SIEMs see only fragments of that movement in any single log source
External attack surface findings and internal hunt data typically live in separate tools, so hunters rarely connect what’s exposed to what’s actually happening inside
Why This Is Different
Threat hunting is fundamentally a data-stitching problem before it’s an investigative-skill problem. A hunter who has to manually correlate network flow, EDR telemetry, and identity logs is spending most of their time on plumbing, not hunting. Because that normalization already happened before the hunt starts, a hunt begins from a unified dataset instead of three separate exports.
Hunting also lives or dies on how much history is actually searchable, and most retention strategies aren’t built with hunting in mind, they’re built for compliance windows. The retention model here is designed for the job: extended, immediately searchable hot storage paired with a long-term lake, so a hunter isn’t limited to whatever a standard SIEM retention window happened to keep on hand. One number worth confirming before this page ships: the live site states 7+ years of immediately available hot storage for hunting specifically, while an internal source describes a much shorter SIEM-hot-plus-long-term-lake model. This page should use whichever figure is actually accurate once that’s reconciled, not both.
The other common approach, making the SIEM smarter or the TIP faster in isolation, doesn’t help a hunter either, because it still treats intelligence and telemetry as two things that only meet manually, alert by alert. A hunt that starts from a hypothesis needs to pivot across both at once, which is exactly what a unified, intelligence-enabled dataset, not just a bigger log store, is built to do.
Threat Hunting Use Cases
Cross-domain lateral-movement hunts
Pain: Attackers move east-west across endpoint, network, and identity boundaries, but most SIEMs see only fragments of that movement in any one log source.
Today: Hunters manually pivot between network flow data and the EDR console, reconstructing movement by hand across tools that weren’t built to talk to each other.
With Anomali: East-west movement traces across network flow, enriched EDR, and IdP data in one pass, the whole path, not the fragments.
External-surface-informed hunts
Pain: External attack surface findings, like exposed assets and shadow infrastructure, are often disconnected from internal hunting, so hunters miss the connection between what’s exposed and what’s happening inside.
Today: Attack surface management findings live in a separate tool from internal hunt data, so hunters rarely tie external exposure to internal telemetry.
With Anomali: External exposure findings tie directly to internal network and EDR telemetry in one hunt, instead of two disconnected investigations run by two different teams.
Proof
A global financial institution that replaced its legacy SIEM outright saw an 8x improvement in visibility and search times that dropped from days to seconds.
FAQ
What named frameworks does Anomali map to for the Threat Intelligence use case?
Every indicator maps to MITRE ATT&CK and normalizes through STIX/TAXII for interoperability, so intelligence is portable in the vocabulary your whole security organization already uses.
What’s the difference between threat hunting and threat detection?
Detection is automated and reactive to known patterns. Hunting is proactive, specifically looking for what automated detection was built to miss.
Do we need a dedicated threat hunting team to use this?
No. It supports dedicated hunters running hypothesis-driven searches and SOC analysts running ad hoc pivots during an active investigation, on the same unified dataset either way.
How does external attack surface data factor into an internal hunt?
Attack surface management findings, like exposed assets or shadow infrastructure, normalize into the same schema as internal telemetry, so a hunter can start from “what’s exposed” and pivot directly into “what’s happening inside” without switching tools.
How far back can a hunt search?
Historical intelligence and telemetry remain searchable over an extended window, not just recent activity, so long-dwelling compromises stay discoverable.
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