Security Operations Built for AI Workloads.

A managed SOC that understands AI threats - with detection rules built for prompt injection, model tampering, and agent exploitation, not just traditional IT attacks.

Duration: Ongoing Team: Dedicated AI Security Analyst + SOC Team

The Challenge

You might be experiencing...

Your existing SOC has excellent coverage of traditional IT threats - but no detection rules for model tampering, prompt injection campaigns, or AI agent exploitation.

Alert fatigue from your SIEM means AI-specific anomalies in model behavior or inference patterns are buried under noise and never reviewed.

Your ML workloads run 24/7 in production, but security monitoring ends at the infrastructure layer - what happens inside your AI systems is invisible.

An adversary could spend weeks probing your LLM application, building a prompt injection chain, and exfiltrating data through model outputs - and your current SOC would never see it.

Enterprise contracts and compliance requirements are calling for documented 24/7 security monitoring of AI systems, and your team has no capacity to build this internally.

AI-powered security operations fills the critical gap between traditional SOC coverage and the actual threat surface of modern AI workloads. Your infrastructure might be perfectly monitored - but if your model APIs, inference endpoints, and AI agents are invisible to your SOC, you have no visibility into the attacks that matter most to your AI business.

What Traditional SOCs Miss

Traditional managed security operations centers were built for a world of servers, networks, applications, and endpoints. Their detection logic is tuned for traditional attack patterns: malware signatures, lateral movement indicators, credential stuffing, data exfiltration via file transfer or network egress.

AI threats don’t look like any of these:

A prompt injection campaign appears as normal API traffic - valid HTTP requests to your model endpoint, standard response bodies. The attack is in the content, not the protocol.
Model enumeration by a competitor or adversary looks identical to legitimate high-volume API usage. The signal is in usage patterns, not request format.
Agent exploitation through tool permission abuse generates activity logs in your cloud environment, your email system, or your file storage - but those events are attributed to your AI agent, not to a human attacker.
Data exfiltration via model outputs has no network signature. The data leaves through the model’s response to an adversarial prompt.

Traditional SOC rules don’t catch these. They weren’t designed to.

Detection Logic Built for AI

Our AI security monitoring service deploys detection rules that are purpose-built for AI threat patterns. Prompt injection detection monitors for adversarial prompt characteristics across model API traffic. Behavioral baselining tracks normal inference patterns - query volumes, response sizes, API access sequences - and flags anomalies. Agent monitoring tracks tool calls and permissions usage against established baselines. Model access monitoring detects enumeration and extraction patterns.

The AI Analyst Difference

Every client gets a dedicated AI security analyst who understands your specific AI architecture, threat model, and business context. This analyst reviews AI-specific incidents that require human judgment beyond automated rules, conducts the monthly threat model review, and is your primary escalation point for AI security questions. General SOC analysts can handle IT incidents; AI incidents require specialist knowledge that only comes from deep focus on the AI security domain.

Our Approach

Engagement Phases

Week 1-2

Assessment

AI workload inventory review, existing monitoring gap analysis, log source identification, alert thresholding discussion, and SOC integration planning.

Weeks 3-6

Onboarding

Log ingestion configuration, SIEM integration, AI-specific detection rule deployment, baseline behavioral profile establishment, and escalation workflow setup.

Ongoing

Active Monitoring

24/7 monitoring of AI workloads, prompt injection detection, model access anomalies, inference pattern analysis, agent behavior monitoring, and L1-L3 incident response.

Ongoing

Continuous Improvement

Monthly detection rule tuning, false positive reduction, new threat research integration, quarterly threat model updates, and semi-annual tabletop exercises.

What You Get

Deliverables

24/7 security monitoring of AI workloads - model APIs, inference endpoints, agent systems, and ML pipelines

AI-specific detection rule library - prompt injection campaigns, model enumeration, excessive API access, behavioral anomalies

L1-L3 incident response - triage, investigation, and containment for AI security incidents

Monthly security reports - incident summary, detection metrics, threat landscape updates

Quarterly threat model reviews - updated AI threat model for your specific workloads

Dedicated AI security analyst as primary point of contact

Expected Outcomes

Before & After

Metric	Before	After
AI Threat Visibility	Zero - AI workloads completely unmonitored at application layer	24/7 monitoring with AI-specific detection rules active
Mean Time to Detect	Unknown - no AI-specific detection capability	AI-specific MTTD tracked and continuously improved
Incident Response	No AI security incident response playbooks exist	L1-L3 response with AI specialist on call 24/7

Technology

Tools We Use

SIEM (Splunk / Elastic / Microsoft Sentinel) Custom AI detection rules Behavioral baselining MITRE ATLAS Automated response playbooks

Common Questions

Frequently Asked Questions

What makes an AI-powered SOC different from a traditional managed SOC?

Traditional managed SOCs are built for IT infrastructure threats: malware, lateral movement, data exfiltration via network channels, credential attacks. They use detection logic designed for these patterns. AI workloads create a completely different threat surface: prompt injection campaigns that unfold over model API calls, model tampering through training pipeline access, agent exploitation through tool permission abuse, and data exfiltration via model outputs. Our SOC is built with detection rules, analyst training, and response playbooks designed specifically for these AI-native threat patterns.

What log sources do you require?

At minimum, we need model API access logs (requests and responses), inference endpoint logs, agent execution logs, and training/serving infrastructure logs. Ideal coverage also includes SIEM integration, cloud provider logs (for ML infrastructure), and data pipeline access logs. During onboarding, we assess your current log coverage and identify gaps - we can operate with partial coverage and improve incrementally.

How does L1-L3 escalation work for AI incidents?

L1 analysts handle initial triage - identifying that an anomaly is a genuine security event versus normal model behavior. L2 analysts investigate confirmed incidents, building the attack timeline and assessing impact. L3 specialists handle complex AI-specific incidents requiring deep expertise: prompt injection campaign analysis, agent exploitation forensics, model integrity assessment. Your dedicated AI security analyst is available for direct communication throughout.

Can you integrate with our existing SOC?

Yes. Many clients have existing SOC coverage for traditional IT infrastructure and engage us specifically to add AI workload coverage. We integrate with your existing SIEM, follow your escalation procedures, and provide AI-specific coverage as a specialist overlay on your existing program.

What compliance requirements does this satisfy?

Documented 24/7 security monitoring of AI systems is increasingly required by enterprise procurement security questionnaires, SOC 2 Type II (covering the AI components of your systems), and emerging regulatory frameworks including EU AI Act requirements for high-risk AI system monitoring. We provide monthly reports and can provide specific compliance documentation for audit purposes.

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