Gurucul REVEAL

Includes behavioral baselining and anomaly detection for users and entities in the core platform, eliminating the need for a separate UEBA product and the associated data movement.

Provides built-in orchestration and automated response through playbooks on alerts and cases rather than requiring a separate SOAR product.

Filters, routes, transforms, and enriches event data in the ingestion pipeline before storage, letting teams drop low-value data and tier the rest to control volume and cost.

Ships vendor-maintained detection rules and use cases mapped to MITRE ATT&CK with minimal configuration, with breadth, accuracy, and update cadence varying across platforms.

Queries event data in external stores, third-party data lakes, or other regions without first ingesting it into the SIEM repository.

Provides prebuilt reports and dashboards mapped to frameworks such as PCI DSS, HIPAA, and GDPR, with out-of-the-box breadth varying across platforms.

Offers on-premises, cloud-hosted, cloud-native, and SaaS deployment options for data residency, sovereignty, and air-gap requirements, with availability varying by platform.

Manages and applies threat intelligence natively to enrich and prioritize detections, supporting vendor-curated and third-party feeds with availability varying by platform.

Investigates, evidences and reports on security alerts with case management to support incident response.

Interoperates with XDR and extended telemetry and response sources such as EDR and NDR.

Normalizes, enriches and risk-scores ingested data from third-party systems such as threat intelligence sources and CMDB.

Stores security event data long term with searchable recall across tiered hot and cold storage, with support for embedded or bring-your-own data lakes varying by platform.

Builds behavioral baselines per user account, device, and application, capturing access timing, resource usage patterns, and activity volumes specific to each entity rather than aggregate thresholds.

Combines multiple weak behavioral signals into a single risk score per user or entity, ranking which accounts warrant investigation so analysts focus on the highest-risk anomalies.

Models attacker-in-residence scenarios (pre-resignation data staging, after-hours privileged access, bulk download exceeding peer norms), with risk scores decaying appropriately for resolved anomalies.

Automatic enrichment and triage of incoming alerts to reduce manual analyst effort and prioritize genuine incidents.

Customizable playbooks that automate and orchestrate repeatable response tasks and multi-step workflows across security and IT tools.

Out-of-the-box connectors and APIs to security and IT systems that let playbooks read context and push enforcement actions.

Centralized case management to plan, track, and coordinate the response to security incidents, storing investigation data and evidence in one workspace.

Aggregation, scoring, and operationalization of threat intelligence feeds to enrich alerts and drive automated response decisions.

Analyzes identity telemetry (authentication events, access patterns, privilege use) in real time with behavioral baselines and risk scoring; leading implementations detect identity attacks in sub-second time.

Executes platform-native response actions to active identity attacks - session revocation, credential reset, account isolation, step-up authentication - automatically or with analyst approval.

Detects credential-abuse techniques that defeat authentication controls, including MFA circumvention, session hijacking, and forged or replayed tokens.

Performs initial triage of incoming alerts automatically, classifying and prioritizing them to cut tier-1 workload before a human touches the queue.

Investigates alerts end-to-end from trigger to verdict and closes them out autonomously, so the full volume of raw alerts gets analyzed without resource-constraint concessions.

Identifies and dismisses false-positive alerts with documented rationale, reducing noise reaching human analysts.

Lets analysts drive investigations and threat hunts through natural-language questions instead of query languages.

Generates investigation summaries and incident reports for analysts and leadership from completed investigation activity.

Recommends the next response actions to take based on investigation findings.

Automatically gathers and attaches context — threat intelligence, asset and identity data — to alerts during triage and investigation.