Knowledge Engine for Security & Cyber Operations

Investigate threats with the full story behind every signal.

Bring alerts, assets, identities, tickets, threat intelligence, policies, and prior investigations into one evidence-backed workflow—from triage to response.

KEP

Threat investigation workspace

Investigate this impossible-travel alert, determine affected resources, and prepare next actions.

I connected identity, endpoint, cloud activity, asset ownership, threat intelligence, and prior investigations. I’m preparing the investigation now.

Identity threat investigationReport

58%

lower token cost

when using Knowledge Engine

2x

more tasks

vs. frontier models

32%

more accurate with open source

vs frontier model

19 of 34

tacit-knowledge facts captured

vs. RAG’s 1

Source: “Knowledge Engine Platform for Long-Horizon Professional Agents” and “Context Graphs for Recovering Tacit Organizational Knowledge”, Accrete AI, August 2026.

A signal is not an investigation.

Security teams have no shortage of alerts. The difficult work is connecting a signal to the affected asset, identity, business service, relevant history, policy, and threat context quickly enough to decide what happens next.

Security & Cyber Operations cognitive fabric

Connect the context behind the work.

Agents learn the language of the work, create the data flows that connect its systems, and build a living context graph for each request.

Agents learn your language

They learn your teams’ terms, shorthand, and ways of working, then create a shared understanding of what everything means.

Agents connect your systems

They create the data flows that bring the right information together for each task, without manual setup for every source.

Agents build the context graph

They connect people, records, decisions, and outcomes while preserving a clear path back to the source.

From domain language and source data to a context graphThe context model resolves jargon, synonyms, abbreviations, and intent into shared entities and relationships. Source mappings connect those concepts to tables and keys while agentic data pipelines prepare durable data. The resulting context graph can be virtualized over its sources or materialized.NATURAL-LANGUAGE REQUEST“Is this travel alerta real account threat?”CONTEXT MODELDomain languageJARGON · SYNONYMS · INTENTATO→account takeovertoken→cloud credentialintent: verify threatCanonical modelENTITIES · RELATIONSHIPSIdentityEndpointResourceUSESACCESSESSemantic bindingsCONCEPT → SOURCE DATAIdentity→OktaEndpoint→CrowdStrikeResource→Wiztables · columns · keysAGENTIC DATA PIPELINESCONTEXT GRAPHOktaCrowdStrikeWizIdentitiesEndpointsCloud eventsJOINThreat contextA. PatelIDENTITYlaptop-42ENDPOINTcloud-tokenCREDENTIALCASE-204CASEVIRTUALIZEDquery sources in placeMATERIALIZEDpersist selected subgraphs
The context model interprets a domain’s vocabulary, synonyms, abbreviations, and intents, then resolves natural-language requests to shared entities and relationships. Source mappings connect those concepts to tables, columns, keys, dialects, and execution integrations. KEP can query the resulting context graph virtually over source data or materialize selected graph data.
  • CrowdStrike
  • Wiz
  • Okta
  • Action1
  • AWS
  • Azure Cloud

Workflows

Change the work, not just the interface.

01

Enrich and prioritize alerts

Combine security telemetry with asset criticality, identity context, ownership, current exposure, and prior activity to determine which signals deserve attention first.

02

Build a source-backed investigation

Trace relevant relationships across systems and produce a brief that separates confirmed facts, derived context, open questions, and recommended next steps.

03

Coordinate governed response

Draft the response plan, assign work, prepare leadership updates, and require review before consequential changes run in a connected system.

One end-to-end example

From isolated signal to evidence-backed response.

  1. 01

    Connect

    Gather the alert, endpoint and identity context, asset ownership, related tickets, threat intelligence, policies, and prior investigations.

  2. 02

    Analyze

    Establish a timeline, identify connected entities and activity, test likely explanations, and surface the evidence behind each material claim.

  3. 03

    Deliver

    Produce an investigation brief with findings, confidence boundaries, affected resources, open questions, and recommended actions.

  4. 04

    Control

    Apply source permissions and organization policies, then ask for approval before containment, ticket, identity, or infrastructure changes.

What the team gets

Outcomes people can inspect and use.

Prioritized alert context

The business, identity, asset, and historical context required to judge urgency.

Investigation brief

A reviewable timeline, findings, source evidence, uncertainty, and recommended response.

Response and audit record

Approved actions, owners, status, and the decision trail preserved for review and reuse.

The Accrete product

Context, work, and control in one path.

The Knowledge Engine provides a governed investigation and workflow layer across approved security and enterprise systems. Specific read and action capabilities depend on the connected provider, account permissions, and organization policy.

Start with one workflow

Define the outcome before expanding the system.

Bring the workflow, its information sources, its approval requirements, and the result your team needs. We’ll map a focused first deployment and the path to reuse.

Discuss this workflow