Sherlocked Security – AI-Augmented MDR

Hyper-automated detection, triage, and response leveraging machine learning, NLP, and behavioral analytics.

1. Statement of Work (SOW)

Service Name: AI-Augmented Managed Detection & Response (MDR)
Client Type: Digital-First Organizations, Tech-Forward Enterprises, Cloud-Native and SaaS-Heavy Environments
Service Model: 24×7 Human + AI Detection & Response Stack
Compliance Alignment: ISO 27001, NIST AI RMF, SOC 2, MITRE ATT&CK

Scope Includes:

Automated detection pipelines powered by machine learning and behavior modeling
Language-model-driven triage of complex alerts and investigations
AI correlation and storyline generation across telemetry sources
Risk-based prioritization using contextual business and threat models
AI-assisted incident response orchestration (auto-contain, escalate, explain)
Continuous tuning based on real-world data, feedback loops, and analyst signals

2. Our Approach

[Detect] → [Prioritize] → [Explain] → [Act] → [Learn]

ML for Detection: Outlier and behavior-based analytics (UEBA, anomaly detection, clustering)
NLP for Triage: Summarize alerts, extract attacker goals, explain threat patterns
LLM-Augmented Decisioning: Triage alerts with reasoning based on asset criticality and threat type
AI Playbooks: Conditional decision trees auto-triggered by threat likelihood and risk
Human Oversight: Expert MDR analysts validate, tune, and guide AI decisions

3. Methodology

Data ingestion from structured and unstructured sources
Time-series modeling of user, device, and workload behavior
AI-driven alert scoring (confidence, criticality, novelty)
Auto-summary and storyline creation using LLMs
Tiered playbook orchestration (automated → semi-automated → analyst escalated)
Feedback loops from investigations, threat intel, and adversarial simulations

4. Deliverables

AI Detection Confidence Scoring Engine
Natural Language Incident Summaries (via LLM)
Alert Novelty and Threat Clustering Reports
Risk-Based Prioritization Dashboards
Automated Response Logs and Actions Taken
Behavioral Baseline Reports
AI Model Drift & Accuracy Metrics (Quarterly)

5. Client Requirements

Endpoint and network telemetry (normalized JSON, syslog, API, agent-based)
Identity and SaaS data with user context
Minimum 90-day telemetry history for AI training (or enrichment via threat data lake)
Asset tagging and criticality information
Authorization for semi-automated containment actions
Approved AI usage policy (optional)

6. Tooling Stack

AI Detection & Automation: Sekoia.io, Exabeam, Vectra AI, Trellix, SentinelOne Purple AI, Palo Alto XSIAM
Behavior Analytics: UEBA modules in Microsoft, Splunk UBA, Gurucul, IBM QRadar AI
LLMs & NLP: OpenAI API, Azure OpenAI, Anthropic Claude for incident summaries and triage
SOAR: Torq, Tines, Cortex XSOAR, Swimlane
Data Platforms: Snowflake, Chronicle, Elastic, Sigma, BigQuery
Threat Intelligence: MISP, Recorded Future, ThreatConnect

7. Engagement Lifecycle

Ingest telemetry and normalize
Deploy ML models and behavioral analytics
Validate AI detection performance and alert scoring
Enable NLP summarization and decision support
Implement automated response actions for high-confidence threats
Monitor model drift and performance quarterly
Conduct red team testing and retraining as needed

8. Why Sherlocked Security?

Feature	Sherlocked Advantage
AI-Native Architecture	Built on top of LLMs, UEBA, and risk modeling for real-time triage
Triage Acceleration	Alerts prioritized and summarized with NLP, saving analyst hours
Precision Detection	Fewer false positives via behavioral + anomaly correlation
Explainable AI Output	LLM-generated, analyst-readable summaries for exec reporting
Integrated Playbooks	AI-detected threats trigger smart playbooks that adapt in real time

9. Use Cases

Use Case 1: Insider Lateral Movement via Service Accounts

Telemetry: Anomalous service account use → new SMB shares accessed → new device fingerprint
AI Behavior Analysis: Behavioral baseline deviation triggers alert
NLP Summary: “Possible internal reconnaissance activity by service account svc_app1 on Finance network.”
Response: Account suspended, network segmented

Use Case 2: SaaS Abuse and Data Exfiltration

Telemetry: O365 activity anomaly + Box download spike + unknown IP
AI Classification: Novel behavior tagged and correlated to threat pattern
NLP Summary: “Exfiltration of sensitive finance files via Box by user jdoe following elevated privileges.”
Response: User locked, legal informed, IOC sweep triggered

10. AI-Augmented MDR Readiness & Ops Checklist

Telemetry Readiness

[ ] Endpoint and network logs available (structured JSON or syslog)
[ ] Identity provider logs (SSO, MFA, login events)
[ ] SaaS activity logs (Google, Microsoft, Dropbox, Salesforce)
[ ] Threat intel feeds integrated
[ ] DNS, VPN, proxy telemetry available
[ ] User/device/asset tagging enabled
[ ] Cloud telemetry (e.g., CloudTrail, Defender for Cloud)
[ ] At least 30–90 days of historical telemetry for baseline models

Detection & Automation

[ ] UEBA and anomaly detection enabled
[ ] ML/AI alert scoring pipelines active
[ ] NLP summarization enabled for alerts/incidents
[ ] Novelty and outlier scoring configured
[ ] Threat correlation logic using ML-based clustering
[ ] Auto-response playbooks with confidence thresholds
[ ] Adversarial behavior simulation results fed back into ML pipeline
[ ] Explainable AI outputs available to analysts

Response Capabilities

[ ] SOAR playbooks tied to AI alert scores
[ ] Endpoint isolation automation tested
[ ] User suspension and identity lockout enabled
[ ] Notification workflows (email, Slack, dashboard) wired
[ ] Human-in-the-loop escalation workflow configured
[ ] LLM-enabled natural language summaries included in tickets

AI Ops & Governance

[ ] Model drift and performance metrics monitored
[ ] False positive / false negative metrics tracked monthly
[ ] Analyst override feedback loops feeding model retraining
[ ] AI usage policies defined (data privacy, model scope)
[ ] Explainability features evaluated for exec and compliance use
[ ] Quarterly AI risk and ethics review

Continuous Improvement

[ ] Red/purple team testing conducted
[ ] IOC and threat intel integrated into new model retraining
[ ] AI detection coverage mapped to MITRE ATT&CK
[ ] LLM outputs reviewed by SOC leads weekly
[ ] Quarterly roadmap review for AI model refresh and new playbooks

AI-Augmented MDR