Autonomous Threat Hunting

Autonomous Threat Hunting Platform

Spharaka delivers continuous, AI-driven threat hunting across your entire environment. Hunt proactively at machine speed with MITRE ATT&CK-mapped hypotheses, behaviour-based hunting, and AI-generated hunt reports.

Explore the Platform

Continuous AI-Driven Threat Hunting

Stop waiting for alerts. Spharaka's Autonomous Threat Hunting agents continuously search your environment for adversary behaviour, novel TTPs, and dormant threats.

Continuous AI-driven threat hunting across endpoint, identity, network, cloud
Proactive threat detection before attacks escalate
MITRE ATT&CK mapped hunting across tactics and techniques
Behaviour-based hunting that catches living-off-the-land activity
IOC and IOA hunting with multi-source threat intelligence
Scheduled threat hunting campaigns aligned to your risk profile

AI-Generated Hunt Reports and Recommendations

Every hunt produces an analyst-ready report with evidence, scope, and recommended actions, automatically routed into AI SOAR for closed-loop response.

AI-generated hunt reports with full evidence chains
Hunt recommendations based on environment-specific risk
Auto-promotion of validated hunts into detection rules
Integration with AI SIEM and AI SOAR for autonomous response
Customer-isolated AI memory for organisation-specific context

How it works

Hunting without an alert to start from

Threat hunting is normally a scarce senior analyst with a hypothesis and a spare afternoon. The scarcity is the problem: the estate changes faster than anyone can hunt across it.

Form a hypothesis

A hunt starts from a proposition about attacker behaviour, not from an indicator. "If an adversary had valid credentials here, what would the authentication pattern look like?" Hypotheses are generated from MITRE ATT&CK techniques, from threat intelligence about active campaigns, and from what this specific estate exposes.

Scope it to this environment

A generic hypothesis is untestable. It is narrowed against the actual estate: these domain controllers, these service accounts, this remote access path, these cloud roles. A hunt that cannot name what it is looking at will find nothing worth acting on.

Test against history

The hunt runs across retained telemetry rather than the live stream, because the point is to find what was already missed. This is where retention policy becomes a detection capability: a ninety-day window cannot answer a question about a dwell time longer than ninety days.

Separate finding from artefact

Most hypotheses return something, and most of it is legitimate. The step that makes hunting useful rather than exhausting is discriminating a genuine finding from the administrative habit that resembles it, which is where behavioural baselines do the work.

Report with evidence

Each hunt produces a written result: what was proposed, what was searched, what was found, what was excluded and why. A negative result is a real result, and recording it is what stops the same ground being covered repeatedly.

Promote what recurs

A hypothesis that finds something worth acting on becomes a standing detection, so the hunt does not have to be re-run to keep the coverage. This is how hunting compounds rather than resetting each cycle.

Inputs and integrations

What a hunt draws on

Hunting is limited by retention and by breadth. A hunt can only ask questions the data can answer.

Where hunts run

Retained endpoint process, file and registry history
Authentication and directory records across the retention window
Network flow, DNS and gateway history
Cloud control plane audit history
SaaS audit trails
Industrial protocol history through Spharaka Signal

What hypotheses come from

MITRE ATT&CK tactics and techniques, mapped to this estate
Threat intelligence on campaigns active in your sector
Behavioural baselines, for what would be abnormal here
Asset and exposure data, for what is actually reachable
Previous hunt results, including the negative ones

What a hunt produces

A written hunt report with the evidence chain
Findings promoted into cases for investigation
New standing detections where a hypothesis proved out
Coverage gaps, where the data could not answer the question
A record of what was excluded and on what grounds

The most valuable output is often the coverage gap. A hunt that cannot be completed because a source is not retained has found a blind spot, which is worth more than most positive findings.

The difference

Continuous hunting against the scheduled kind

Almost every security team says it hunts. In practice hunting is what happens when a senior analyst has a quiet week, which is why coverage is uneven and undocumented.

Aspect	Analyst-led hunting	Spharaka
How often it happens	When someone senior has time. In a busy quarter, not at all, and nobody notices because there is no output to miss.	Continuously, against a hypothesis backlog that does not depend on anyone's availability.
Coverage	Whatever the hunter happened to think of, shaped by their background and last incident.	Systematically mapped to ATT&CK tactics and techniques, so gaps in coverage are visible rather than assumed away.
Depth in time	Bounded by how much history a query can practically search before the analyst gives up waiting.	Bounded by retention, not by patience, which is what makes long dwell times findable at all.
Negative results	Rarely recorded, so the same ground gets covered repeatedly and nobody can say what has been checked.	Recorded with the same weight as positive ones, because knowing where you have looked is half the value.
Living-off-the-land activity	Findable, if the hunter thinks to ask about the specific tool and has time to sift the legitimate use.	The main case it is built for: legitimate tools used in illegitimate sequences, discriminated against baselines.
What happens to a finding	Written up, sometimes turned into a detection, often lost when the analyst moves on.	Promoted into a standing detection, so the coverage persists without the hunt being re-run.

Worked example

A hunt with no indicator to search for

The hypothesis: if an adversary had a foothold on a domain-joined host, they would enumerate the directory using tooling already present on the system.

Scope

Narrowed to built-in Windows tooling capable of directory enumeration, across all domain-joined hosts, over the full retention window. No hash, no domain, no IP, because the technique uses nothing that is not already installed.

Returns roughly eleven thousand executions. Almost all of it is legitimate: administrators, inventory scripts, software distribution, and a monitoring agent that does it hourly on every host.

Discriminate

Baselines separate the habitual from the exceptional. Which hosts do this routinely, which accounts, at what hours. Eleven thousand executions reduce to nineteen that do not fit any established pattern.

Correlate

Of the nineteen, three occurred on hosts that also showed an unusual authentication within the preceding hour, from an account that does not administer them.

Conclude

Two resolve to a contractor performing genuine work outside their normal hours, confirmed against the change record. One does not resolve, and becomes a case.

Promote

The discrimination logic that reduced eleven thousand to nineteen becomes a standing detection, so the next occurrence is caught live rather than at the next hunt.

The hunt found one thing worth investigating and produced a permanent detection as a by-product. Neither outcome was reachable by searching for an indicator, because the technique deliberately uses software the organisation installed itself.

Evaluating this

What to ask a hunting platform

Hunting is the easiest capability to claim and the hardest to demonstrate, because a good hunt often finds nothing.

Show me a hunt that found nothing

Ask for the report. Whether a negative result is documented to the same standard as a positive one tells you whether hunting is a real process here or a search bar with a name on it.

How far back can it actually look?

Retention and query practicality are different limits. Ask how long a hunt across the full window takes, because a capability that is technically possible and takes eleven hours is not one anyone will use.

Which ATT&CK techniques are not covered?

A vendor who claims full coverage is describing a matrix, not a capability. The useful answer names the techniques the available telemetry cannot test for, which is also your data collection roadmap.

What happens to a finding afterwards?

Ask to see a detection that exists because a hunt produced it. If findings do not become standing detections, every hunt is starting from zero and the coverage never accumulates.

Questions

Frequently asked questions

What is Autonomous Threat Hunting?

Autonomous Threat Hunting is the use of agentic AI to continuously and proactively hunt for adversary behaviour across your environment, without depending on alerts or manual hypotheses.

How is AI Threat Hunting different from traditional threat hunting?

Traditional threat hunting depends on human-built hypotheses run on a periodic basis. AI Threat Hunting runs continuously, generates and validates hypotheses autonomously, and produces analyst-ready reports.

Is Spharaka's threat hunting mapped to MITRE ATT&CK?

Yes. Every hunt, detection, and report is mapped to MITRE ATT&CK tactics and techniques for unified coverage reporting.

Can validated hunts become detections?

Yes. Validated hunts can be promoted to AI SIEM detection rules automatically, continuously improving your AI SOC detection coverage.

See Spharaka Autonomous Threat Hunting in action

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