Overview#
A security operations centre processing 50,000 alerts a day cannot treat them equally. Missing a genuine P1 buried in noise has consequences measured in breach impact and regulatory censure. The AI-Powered Alert Triage and Prioritisation system addresses this directly: it applies machine learning to score every incoming alert, routes P1 events immediately to the on-call analyst, and filters low-confidence noise before it ever reaches a human queue.
Purpose-built for high-volume security operations, the platform transforms overwhelming alert floods into prioritised, actionable intelligence streams. Critical threats receive immediate attention. False positives are automatically filtered. And every triage decision comes with a clear explanation of why it was made.
Key Features#
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ML-Based Priority Assignment (P1-P5): Ensemble machine learning models trained on historical alerts predict alert disposition, priority level, and threat severity. Priority levels range from P1 (Urgent, immediate response) to P5 (Informational, log only), ensuring critical threats receive attention before they escalate.
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Sentiment and Threat Analysis: Advanced natural language processing analyses alert text, entity descriptions, and evidence narratives to extract urgency sentiment, identify threat actor tactics via MITRE ATT&CK mapping, and detect narrative patterns indicating coordinated campaigns.
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Automated Alert Routing: Intelligent routing assigns alerts to the most appropriate analyst based on expertise, workload, availability, and alert characteristics. Skill matrix matching and learning algorithms continuously improve first-assignment accuracy.
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Confidence Score Calibration: Transparency into model certainty enables analysts to trust high-confidence recommendations while applying human judgement to ambiguous cases. Ensemble model agreement and SHAP-based explainability show contributing factors for every recommendation.
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Continuous Model Learning: Weekly retraining on new labelled alerts and analyst feedback ensures models adapt to evolving threat landscapes. Routing accuracy improves continuously through feedback loops.
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Explainable AI Recommendations: Every triage decision includes reasoning, contributing factors, and model metadata so analysts understand why a priority was assigned, building trust and satisfying audit requirements.
Use Cases#
Security Operations Centres#
SOCs processing thousands of alerts daily use AI triage to cut analyst workload, ensure critical threats are handled first, and maintain consistent prioritisation across shifts. Teams handle significantly more alerts with AI assistance while reducing false positive fatigue.
Financial Crime Units#
Banks and financial institutions with compliance-mandated alert review use automated triage to prioritise sanctions matches, suspicious transaction patterns, and regulatory alerts, ensuring SLA compliance and audit readiness.
Managed Security Service Providers#
MSSPs supporting multiple clients apply AI triage to scale alert processing across client environments, route alerts to appropriate specialists, and maintain service quality as alert volumes grow without proportional headcount increases.
Critical Infrastructure Operators#
Utilities and critical infrastructure operators facing nation-state and ransomware threat actors use AI triage to prioritise OT/IT boundary alerts, cutting mean time to respond on intrusion indicators that would otherwise arrive late in a crowded queue.
Integration#
Programmable API access is available for triggering AI triage on individual or bulk alerts, retrieving priority distributions and sentiment analysis, managing automated routing and reassignment, and monitoring model performance. The platform integrates with SIEM platforms, threat intelligence feeds, and case management tools via OAuth 2.0 and JWT authentication with AI operation permissions and model version access control.
Open Standards#
- MITRE ATT&CK: Triage decisions store and expose mapped tactics and technique identifiers (
mitre_tactics,mitre_techniques) on every alert, enabling analysts to trace priority assignments directly to the adversary behaviour framework. - OASIS STIX 2.1 / TAXII 2.1: Triaged alerts are exportable as STIX 2.1 indicator and observable bundles; the platform polls external TAXII 2.1 feeds to enrich triage context with live threat intelligence.
- GraphQL (June 2018 specification): All triage operations, including priority assignment, routing, feedback recording, and accuracy monitoring, are exposed as typed GraphQL queries and mutations via the Strawberry schema layer.
- OAuth 2.0 (RFC 6749) and JSON Web Tokens (RFC 7519): API access to triage and model-management endpoints is secured with RS256 JWT bearer tokens issued under OAuth 2.0 scopes, with per-operation permission classes enforced at the GraphQL resolver level.
- RFC 3161 (Internet X.509 PKI Time-Stamp Protocol): Audit-trail records and exported alert evidence produced by the triage engine are cryptographically timestamped against a qualified Time-Stamp Authority, supporting legal admissibility and regulatory review requirements.
- ISO 8601: All triage record timestamps, feedback events, and model-performance metrics are serialised in ISO 8601 format to ensure interoperability with SIEM platforms and downstream case-management tools.
- ISO/IEC 27001: The platform's model governance, audit trails, and access controls are designed to support ISO/IEC 27001 certification, with triage decision logs contributing to the information security management evidence base.
Security & Compliance#
Model governance includes version control, audit trails, and bias testing. PII is removed from training data. The platform supports EU AI Act compliance for high-risk AI systems, SOC 2 Type II, and ISO 27001 certifications. Complete audit documentation supports regulatory review requirements.
Last Reviewed: 2026-02-23 Last Updated: 2026-04-14