Integration

AI Integration for SentinelOne and SOAR AI Integration

A practical guide to embedding AI decision engines within SOAR platforms to automate SentinelOne alert processing, execute conditional playbooks, and update cases, reducing SOC analyst workload.

Get in touch Learn more

Operations team reviewing AI vendor onboarding platform on laptop, forms and contracts visible, casual office workspace.

ARCHITECTURE BLUEPRINT

Where AI Fits Between SentinelOne and Your SOAR Platform

A technical guide to embedding an AI decision layer between SentinelOne alerts and SOAR playbooks for autonomous triage and response.

The integration point is the SentinelOne Singularity API, which streams enriched alert data (from Deep Visibility, Storyline, and Cloud Workload Protection) to a dedicated AI processing queue. The AI agent acts as an intermediary, consuming these raw JSON payloads to perform three core functions: alert summarization (condensing technical telemetry into a plain-language narrative), confidence scoring (assigning a risk score based on behavioral indicators and threat intelligence context), and playbook selection (mapping the scored alert to a specific, parameterized workflow in your SOAR platform, such as Palo Alto XSOAR, Splunk SOAR, or ServiceNow SecOps).

For a production implementation, the AI layer is deployed as a containerized service that calls the SOAR platform's REST API to execute the selected playbook. A critical workflow is conditional containment: the AI evaluates the alert's confidenceScore and threatCategory. If thresholds are met, it automatically triggers a SentinelOne isolate or kill action via the SOAR, while lower-confidence alerts are routed to a human-in-the-loop approval queue in the SOAR interface. This architecture ensures actions are logged in both the SOAR's audit trail and SentinelOne's Activity Log, maintaining a clear chain of custody. For advanced use, the AI can also query SentinelOne's DataSet for historical context before making a decision, creating a feedback loop where past incident outcomes inform future automation.

Governance is managed through the SOAR platform's native Role-Based Access Control (RBAC). Approval workflows, execution limits, and policy overrides (e.g., never auto-isolate servers in a specific asset group) are configured within the SOAR, not the AI service. This keeps security policy centralized and auditable. Rollout typically follows a phased approach: starting with read-only summarization to build trust, moving to playbook suggestion for analyst review, and finally graduating to fully automated execution for high-fidelity, repetitive threats like commodity ransomware or coin miners. This staged deployment allows teams to calibrate the AI's decision logic against real-world data without risking operational disruption.

ARCHITECTURAL BLUEPRINT

Key Integration Surfaces: SentinelOne APIs and SOAR Hooks

SentinelOne Threat APIs for AI Ingestion

The core of AI integration begins with the SentinelOne Threats API (/web/api/v2.1/threats) and Incidents API (/web/api/v2.1/incidents). These endpoints provide the raw signal data for AI analysis.

Key Data Objects for AI:

threatInfo: Contains detection details like file path, SHA256, confidence level, and MITRE ATT&CK mapping.
agentRealtimeInfo: Provides endpoint context (hostname, OS, network, logged-in user).
incidentStatus: Tracks the investigation lifecycle (New, In Progress, Resolved).

AI agents typically poll or consume webhooks from these APIs to fetch new, high-severity threats. The AI's first job is to enrich and triage—correlating the threat with internal asset criticality data and past incidents to assign a business-risk priority score, moving beyond the default CVSS score.

INTEGRATION PATTERNS

High-Value AI Use Cases for SentinelOne + SOAR

Embedding AI decision engines within SOAR platforms to process SentinelOne alerts, execute complex playbooks on the Singularity platform, and update cases. These patterns move from reactive, manual workflows to autonomous, context-aware response.

AI-Powered Alert Triage & Enrichment

AI analyzes raw SentinelOne alerts (e.g., Malicious Behavior, Suspicious Script) in real-time. It cross-references Deep Visibility telemetry, external threat intel, and asset criticality to assign a dynamic risk score. High-confidence, high-severity alerts are automatically forwarded to SOAR with enriched context, while low-priority noise is suppressed or sent to a review queue.

Hours -> Minutes

Triage time

Dynamic Playbook Selection & Execution

Instead of static if-then playbooks, an AI agent evaluates the full context of a SentinelOne incident—including Storyline forensic data, affected user role, and time of day—to select and parameterize the optimal SOAR playbook. It can dynamically choose between isolation, scripted remediation, forensic collection, or simply creating a case for analyst review.

Batch -> Real-time

Decision logic

Automated Threat Investigation & Narrative

For complex incidents, AI uses the SentinelOne Investigate API to query Deep Visibility, automatically building a timeline of related events. It synthesizes this into a plain-English investigation summary, highlighting the root process, lateral movement, and key IOCs. This narrative is attached to the SOAR case, giving Tier 1 analysts a head start.

1 sprint

Investigation prep

Conditional Containment with Human-in-the-Loop

AI evaluates the risk of automated containment actions (like network isolation via SentinelOne's Threat Isolation). For high-confidence malware, it executes immediately. For ambiguous cases (e.g., a critical server), it routes a request with justification to an analyst in the SOAR console for one-click approval, blending speed with control.

Same day

Containment latency

Post-Incident Reporting & Knowledge Capture

After an incident is resolved, AI analyzes the SOAR case notes, SentinelOne activity logs, and manual actions taken. It generates a structured after-action report and suggests updates to detection rules or playbook logic. These insights are fed back into the SOAR platform as recommended improvements, closing the feedback loop.

Natural Language Query & Action Interface

An AI copilot embedded in the SOAR interface allows analysts to ask questions like "Show me all endpoints with suspicious PowerShell activity in the last 24 hours" or "Isolate endpoint X and collect a memory dump." The AI translates this into Singularity Platform API calls and executes the actions, updating the SOAR case log.

PRACTICAL AUTOMATION PATTERNS

Example AI-Driven SOAR Workflows for SentinelOne

These workflows illustrate how AI agents can be embedded within SentinelOne Singularity's automation layer to evaluate alerts, execute complex playbooks, and update cases with minimal human intervention. Each pattern is designed to integrate with the Singularity Platform's APIs and data model.

This workflow uses AI to filter noise, enrich alerts with forensic context from Storyline, and route only high-confidence incidents for human review.

Trigger: A new SentinelOne alert is created in the Singularity Platform (e.g., Malicious Behavior Detected).
Context Pulled: The AI agent calls the SentinelOne API to retrieve:
- The alert details (/web/api/v2.1/cloud-detections).
- The associated Storyline forensic data (/web/api/v2.1/threats).
- Endpoint information and recent process tree.
AI Agent Action: The agent analyzes the Storyline data to answer key questions:
- Is this part of a known, benign software update chain?
- Does the process tree show lateral movement or data exfiltration patterns?
- What is the confidence level (High/Medium/Low) based on TTP alignment?
System Update: Based on the analysis:
- High Confidence Malicious: Agent automatically initiates a Contain Network action via the SentinelOne API (/web/api/v2.1/remote-actions/contain-network) and creates a high-priority case in the connected SOAR platform (e.g., ServiceNow).
- Low Confidence / Benign: Agent adds an analyst note to the SentinelOne threat: "AI Triage: Isolated behavior, likely benign software update. No further action recommended." and closes the alert.
Human Review Point: All Medium confidence alerts are routed to a dedicated SOC queue with the AI's analysis appended, requiring analyst decision.

CONNECTING AI TO THE SOAR AND EDR DATA LAYER

Implementation Architecture: Data Flow and AI Layer

A practical blueprint for wiring AI agents into the SentinelOne Singularity platform and your SOAR to automate complex threat response.

The integration architecture connects three core layers: the SentinelOne Singularity Platform (Deep Visibility, Storyline, Singularity Data Lake), an AI Orchestration Layer (handling reasoning, tool calling, and workflow logic), and your SOAR or Case Management System (like ServiceNow, Splunk SOAR, or Cortex XSOAR). Data flows bidirectionally: SentinelOne alerts and enriched telemetry are streamed to the AI layer via webhook or API queue. The AI agent analyzes the alert context, queries Deep Visibility for related events, and uses the Storyline forensic data to reconstruct the attack chain. Based on this analysis, it can execute pre-approved actions directly via the SentinelOne Singularity Complete automation APIs—such as isolating an endpoint, killing a process, or collecting forensic artifacts—and then update the corresponding case in your SOAR with a detailed narrative and evidence summary.

Key implementation details involve secure, policy-aware tool calling. The AI layer acts as a reasoning engine, not a blind automaton. For each potential action (e.g., network containment), it evaluates confidence scores, checks against predefined policy rules (RBAC, approved playbooks, critical asset lists), and can route high-risk decisions for human approval via the SOAR ticket before execution. This is managed through a dedicated tool-calling framework (e.g., using MCP, LangChain Tools, or custom adapters) that securely interfaces with the SentinelOne REST API and your SOAR's automation modules. The architecture includes an audit log for all AI-driven decisions and actions, stored within the SOAR case for full traceability.

Rollout typically follows a phased approach: start with read-only summarization and triage (AI analyzes alerts and drafts investigation summaries for analyst review), progress to assisted response (AI suggests actions for analyst approval and execution), and finally implement conditional autonomous response for well-defined, high-confidence scenarios. Governance is critical; establish a clear AI Security Policy that defines allowable actions, confidence thresholds, and required human-in-the-loop steps for different alert severities and asset criticalities. This ensures the integration scales analyst effectiveness without introducing unacceptable risk.

SENTINELONE AND SOAR INTEGRATION PATTERNS

Code and Payload Examples

Ingesting SentinelOne Alerts into SOAR

A SOAR platform like Splunk SOAR, Palo Alto XSOAR, or ServiceNow SecOps ingests SentinelOne alerts via its Threat Intelligence API or webhooks. The AI layer acts as a pre-processor, analyzing the raw JSON payload to determine severity and routing.

Key Payload Fields for AI Analysis:

agentDetectionInfo.threatName: The malware or threat classification.
agentDetectionInfo.filePath: The endpoint file path involved.
agentDetectionInfo.iocHitIps/Domains: Any network IOCs.
agentDetectionInfo.storylineId: The forensic timeline ID for deeper investigation.

AI Triage Logic: The AI evaluates these fields against internal threat intelligence and asset criticality (often from a CMDB) to assign a priority score and recommend a playbook.

json
// Example SentinelOne Webhook Payload Snippet for AI Analysis
{
  "eventType": "THREAT",
  "data": {
    "agentDetectionInfo": {
      "threatName": "Trojan.Generic",
      "filePath": "C:\\Users\\Public\\temp.exe",
      "storylineId": "STORYLINE_ABCD1234",
      "iocHitIps": ["185.1.2.3"]
    },
    "endpointName": "Finance-Laptop-12",
    "siteName": "Corporate-NA"
  }
}

The AI can flag this as high priority if the endpoint is in a finance group and the IP is on a known C2 blocklist, automatically creating a high-severity SOAR incident.

AI-Enhanced SOAR for SentinelOne

Realistic Time Savings and Operational Impact

This table illustrates the measurable impact of integrating AI agents with SentinelOne's Singularity platform and SOAR automation, focusing on analyst workflows from alert to closure.

Workflow Stage	Before AI Integration	After AI Integration	Implementation Notes
Alert Triage & Prioritization	Manual review of 100+ daily alerts	AI pre-scores & routes 80% of alerts	AI uses Deep Visibility context; high-severity alerts flagged for immediate review
Initial Threat Investigation	Analyst manually queries Storyline & builds timeline (30-60 mins)	AI auto-correlates events & drafts incident narrative (5 mins)	Narrative includes IOCs & affected hosts; analyst validates and edits
Containment Action Recommendation	Analyst researches & decides on isolation/quarantine	AI evaluates threat confidence & suggests specific API actions	Actions (e.g., process kill, host isolate) presented for one-click approval in SOAR
SOAR Playbook Execution	Manual playbook selection & parameter entry	AI dynamically selects & parameterizes playbooks based on alert context	Integrates with Singularity Complete automation; reduces playbook configuration errors
Case Documentation & Handoff	Manual entry of notes, evidence, and actions taken	AI auto-generates case summary with timeline, actions, and context	Summary populates SOAR case; ensures consistent audit trail for shift change
Post-Incident Reporting	Manual compilation of data for weekly/monthly reports	AI synthesizes incident metrics & drafts executive summary	Report highlights trends, MTTR impact, and containment efficacy
Policy & Detection Tuning	Periodic manual review of false positives & missed detections	AI analyzes alert outcomes to suggest policy/rule adjustments	Recommendations feed into SentinelOne policy management; continuous feedback loop

ARCHITECTING FOR PRODUCTION

Governance, Security, and Phased Rollout

A practical guide to implementing AI-driven SOAR for SentinelOne with enterprise-grade controls and a low-risk adoption path.

Integrating AI with SentinelOne's Singularity platform and your SOAR tool requires a clear security boundary and data governance model. The AI agent should operate as a privileged, non-human service account within SentinelOne, with permissions scoped strictly to the APIs needed for alert ingestion (e.g., GET /web/api/v2.1/threats), playbook execution (POST /web/api/v2.1/remote-scripts/execute), and case updates. All AI-generated actions—like running a script to isolate an endpoint or updating a threat's analyst verdict—must be logged to SentinelOne's audit trail and, if required, pass through a human-in-the-loop approval queue in your SOAR platform before execution. This ensures every AI-suggested action is attributable and reversible.

A phased rollout mitigates risk and builds organizational trust. Start with Phase 1: AI-Assisted Triage, where the agent ingests SentinelOne threats, enriches them with external intelligence, and generates a summary with a confidence-scored recommendation (e.g., 'Isolate - High Confidence'). All actions remain manual. In Phase 2: Conditional Automation, implement rules-based automation for high-confidence, low-risk actions, such as tagging a threat as a 'Benign' false positive based on AI analysis of the Storyline. Finally, Phase 3: Autonomous Playbooks enables the AI to parameterize and execute complex SOAR playbooks, but only for pre-defined scenarios and with mandatory post-action reporting sent to a designated Slack channel or SIEM case.

Governance is continuous. Establish a weekly review of the AI's action log and recommendation accuracy. Use SentinelOne's Deep Visibility to audit the outcomes of AI-triggered containment scripts. This feedback loop is critical for tuning prompts, adjusting confidence thresholds, and updating the SOAR playbook library. By treating the AI integration as a continuously monitored subsystem, you gain the efficiency of automation without sacrificing the oversight required for critical security operations.

Enabling Efficiency, Speed & Accuracy

Intelligent Analysis, Decision & Execution

We build AI systems for teams that need search across company data, workflow automation across tools, or AI features inside products and internal software.

Talk to Us

Search across company data

Give teams answers from docs, tickets, runbooks, and product data with sources and permissions.

Useful when people spend too long searching or get different answers from different systems.

Enterprise searchRAGPermissions

Automate internal workflows

Use AI to route work, draft outputs, trigger actions, and keep approvals and logs in place.

Useful when repetitive work moves across multiple tools and teams.

AI agentsWorkflow automationGovernance

Add AI to products and internal tools

Build assistants, guided actions, or decision support into the software your team or customers already use.

Useful when AI needs to be part of the product, not a separate tool.

AI integrationDecision supportModel routing

SENTINELONE AND SOAR AI INTEGRATION

Frequently Asked Questions

Practical questions about embedding AI agents within SOAR platforms to automate SentinelOne alert processing, execute playbooks, and update cases.

This workflow uses an AI agent to evaluate incoming SentinelOne alerts and determine the appropriate playbook or analyst assignment.

Trigger: A new alert is created in SentinelOne Singularity and sent to the SOAR platform via webhook or API poll.
Context Pulled: The AI agent retrieves the full alert context from SentinelOne's API, including:
- Storyline data: Process tree, file modifications, registry changes, and network connections.
- Deep Visibility: Raw endpoint telemetry preceding the alert.
- Threat Intelligence: MITRE ATT&CK mapping and severity score.
AI Action: The agent analyzes the context using a classification model to:
- Determine if the alert is a true positive or likely false positive.
- Assign a priority score (Critical, High, Medium, Low).
- Tag the alert with relevant MITRE techniques (e.g., T1055, T1562).
- Recommend the most relevant pre-built SOAR playbook (e.g., "Isolate Endpoint," "Collect Forensic Artefacts," "User Account Review").
System Update: The agent updates the SOAR case with its analysis and automatically initiates the recommended playbook or routes the case to the appropriate analyst queue.
Human Review Point: For high-confidence false positives or low-severity items, the agent can suggest auto-closure, requiring a supervisor's one-click approval in the SOAR interface.

About the author

Prasad Kumkar

CEO & MD, Inference Systems

Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.

His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.

Limited slotsGet a Free AI Consultation

How We Work

Custom AI workflows for your Business

One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.