Agentic Threat Hunting
Your rules catch the known. Hunting catches the rest. Agentify your threat hunting processes. Better outcomes from the SIEM, EDR, and data lake you already have, with no rip-and-replace.
Pre-built micro agents for hypothesis generation, query execution, lead triage, and pivoting, or build your own.
Custom approval flows, web forms, and case management around any of it. Audit trail on every change.
Purpose-Built Threat Hunting Agents
Configure micro-agents that understand your SIEM, environment, and threat hunting workflows.
A specialized micro agent for every major SIEM.
A generic agent writes generic queries, and generic queries are slow, expensive, and burn through your search quota. BlinkOps ships a micro agent per SIEM, trained on that platform's query language and its cost model. It writes the query the way your platform wants it, scoped and efficient, so a continuous hunt does not blow up your search bill.
Query-language native
SPL, KQL, AQL, ES|QL, UDM. The agent writes in your platform's dialect, not a lowest-common-denominator wrapper.
Quota and cost aware
Scopes time ranges, fields, and indexes so continuous hunting does not burn your search budget.
Schema-aware
Mapped to your data model, so queries run against your fields with no manual translation.
Featuring the most commonly used agents for Threat Hunting
Each agent has a focused role, a knowledge base specific to your environment, and a tightly scoped set of abilities. They generate, they run, they score, and they hand people a lead worth their time.
Hypothesis Agent
Turns intel into testable hunts
Takes threat intel and observed TTPs and turns them into scoped, testable hypotheses, mapped to MITRE and prioritized by relevance to your stack.
Intel-to-hypothesis mapping
MITRE-aligned scoping
Prioritized for your environment
Query Agent
Runs the hunt across your data
Translates each hypothesis into platform-native queries for your SIEM, EDR, and data lake, then runs them. Schema-aware, so there is no manual translation per tool.
Per-SIEM micro agents, quota-aware
Runs across SIEM, EDR, data lake
Schema-aware, no rewrites
Lead Triage Agent
Separates leads from noise
Scores and clusters the hits, suppresses the noise, and promotes only real leads to cases. People review leads, not thousands of raw query rows.
Hit scoring and clustering
Noise suppression
Promotes real leads to cases
Pivot Agent
Maps the full scope
Expands a confirmed lead across entities, time, and data sources to map the whole picture before it lands with IR. The case arrives scoped, not as a single alert.
Entity and timeline expansion
Cross-source correlation
Scopes before handoff to IR
Your coverage cannot keep pace with the technique surface.
Detection rules catch the behavior you have already seen and written for. The set of techniques used in the wild grows faster than any team can write rules. The space between the two is where intrusions live, and the only way to look there is to hunt for it.
From intel signal to confirmed lead.
Agentic workflows that turn intel into hypotheses, run them across your data, score the hits, and hand people a confirmed lead instead of a raw query result. Continuous coverage, with human validation where it matters.
Intel in
Hunt
Confirm with people
Scoped
Close the loop
Hunt continuously. Confirm with people.
Continuous coverage
The whole library runs, not one hunt.
Find the unknown
Behavior no rule was written for.
Analyst Leverage
People validate, agents do the plumbing.
Repeatable and auditable
Every hunt logged and re-runnable.
One platform. The building blocks behind every hunt.
Agentic threat hunting is not a single agent. It is dashboards, tables, case management, agents, workflows, and the channels your hunters already work in, packed into one end-to-end solution.
Hypothesis library, MITRE mappings, prior results, and entity context the agents query.
Every hunt tracked as an investigation, from hypothesis to confirmed or cleared, with full timeline.
Generate hypotheses, translate them to queries, score the hits, and pivot on confirmed leads.
Deterministic execution. Pull telemetry, enrich, run queries, and open cases.
Analysts kick off a hunt or submit a hypothesis without writing the plumbing.
Hunters refine a hypothesis in Slack or Teams and get the query and results back in thread.
SIEM, EDR, data lake, threat intel, and case management connected through one engine.
One hunt a week vs. the whole library, daily.
Same data. Two hunting models. The difference is how much of the technique surface actually gets looked at.
Manual hunting
Hypothesis → Write query → Run → Triage
A senior analyst, one hypothesis at a time, when they have the time. Writing the query, running it, then digging through the rows by hand. Most of the surface never gets hunted.
→
A handful of hunts a quarter
Agentic hunting with Blink
Library → Run continuously → Leads as cases
The standing library runs on a cadence across your data. Hits are scored and clustered, noise is dropped, and only confirmed leads reach a person, already scoped.
→
The full library, every day
vs
Why continuous hunting tilts the field.
Adversaries get AI too. The advantage goes to the defender who can run more hypotheses, faster, against their own data, without burning out the team.