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AI SOC Monitoring
AI SOC Monitoring
Cyber warfare, manual monitoring is no longer enough to stop AI-accelerated attacks. i6 introduces the next evolution: AI Continuous SOC & Incident Response. Our service is an Agentic Defense System specifically built to shield LLMs, RAG pipelines, and Vector Databases.
What we Cover under Monitoring?
List of Log Sources and its valuable realisation under SOC Monitoring:
Layer 1
The Semantic Layer
- Prompt & Input Logs (Hidden system messages)
- Completion & Output Logs (Pre/Post filtering)
- Sentiment & Intent Analysis (Jailbreak patterns)
- System Prompt Leakage Logs
Layer 2
The Model Layer
- Token Consumption (Denial of Wallet prevention)
- Model Parameter Logs (Temp/Top-P monitoring)
- Hallucination & Grounding Scores
- Logit Bias Telemetry (Weight tampering detection)
Layer 3
The Data & RAG Layer
- Vector Database Audit Trails (Pinecone/Milvus)
- Similarity Search "Distance" Metrics
- Data Lineage & Ingestion Logs
- S3/Blob Storage Access Logs
Layer 4
Application & API Layer
- API Gateway Request/Response Metadata
- WAF Logs (SQLi/XSS in AI payloads)
- Session Management & Fingerprinting
- Plugin & Tool-Call Tracing
Layer 5
The Agentic Layer
- Non-Human Identity (NHI) Logs
- Chain-of-Thought (CoT) Traces
- Privilege Escalation (Excessive Agency)
- Human-in-the-Loop (HITL) Logs
Layer 6
Infrastructure Layer
- Kubernetes & Container Orchestration Logs
- Server Host Logs (SSH/Sudo/Integrity)
- CPU/RAM/Disk exfiltration telemetry
- Environment Variable/Secrets Monitoring
Layer 7
Physical Layer (GPU)
- GPU/NPU Utilization (Crypto-jacking detection)
- Hardware Telemetry (Thermal/Voltage spikes)
- VRAM Usage (Model DoS tracking)
Layer 8
Network Layer
- Egress Traffic (C2 Detection)
- DNS Query Logs (Malicious domains)
- VPN & SSO Access Tracking
- Encrypted Traffic Metadata Analysis
The i6 AI Security PDCA Framework
| Phase | Activity | Layer Mapping |
|---|---|---|
| PLAN | Define baselines, RAG policies, configure 8-layer ingestion. | Layers 1, 2, 4, 8 |
| DO | Real-time monitoring, deploy Guardrails and Agentic monitors. | Layers 3, 5, 6, 7 |
| CHECK | Analyze Semantic Drift, Hallucinations, and Vector DB anomalies. | All 8 Layers |
| ACT | Patch prompt vulnerabilities, tune thresholds, rotate keys. | Continuous Feedback |
i6 AI Incident Management Process
1. Detection: SIEM flags violation (e.g., Jailbreak).
2. Suppression: Guardrails kill session instantly.
3. Investigation: Analysts audit Chain-of-Thought logs.
4. Eradication: Removal of poisoned data/Hardening.
5. Recovery: Restore to known good state.
i6 AI Incident Case Study
Layer 3 (RAG) Data Extraction: INC-2026-039
Threat: Automated Botnet seeking Q4 Financial Forecasts.
1. Detection (Check): Vector DB Similarity scores flagged "mathematically adjacent" queries asking for resource allocations without using restricted keywords.
2. Suppression (Do): Contextual Sandbox triggered; attacker received synthetic data while API keys were throttled.
3. Investigation (Act): Agentic Traces showed the AI was "Socially Engineered" due to missing metadata tags on a PDF.
4. Remidiation (Plan): Updated RAG policy to require MFA for financial queries.
Performance & Response SLAs
| Severity | Incident Type | Detection (MTTD) | Suppression | Human Analysis |
|---|---|---|---|---|
| Critical (P1) | Jailbreak, PII Leak, Hijacking | < 1 Minute | Immediate | < 15 Mins |
| High (P2) | Harvesting, GPU Spikes | < 5 Minutes | < 2 Mins | < 30 Mins |
| Medium (P3) | Hallucination Spikes | < 15 Minutes | N/A | < 2 Hours |
| Low (P4) | Routine Audits | < 1 Hour | N/A | < 8 Hours |
Uptime & Commitments
- SOC Availability: 99.99% Monitoring Uptime.
- Guardrail Latency: < 50ms (No UX compromise).
- False Positive Rate: < 2% through continuous tuning.
