Defend AI with AI.
AI-powered security operations for companies running ML in production. Autonomous detection, real-time response, and continuous monitoring for your models, pipelines, and agents.
AI Security Operations Built for AI Workloads
Traditional SOCs were built for networks and endpoints. secops.qa is purpose-built for AI-native threats - model compromise, pipeline tampering, LLM abuse, and agent gone rogue.
AI-Powered SOC
Security operations purpose-built for AI workloads - real-time ML monitoring, AI-specific detection rules, and expert analyst response. 24/7 coverage for your production AI systems.
Autonomous Detection & Response
ML-driven threat detection that learns your model behavior baselines and automatically responds to AI-specific attacks - no manual tuning, no alert fatigue.
Runtime Protection
Runtime security controls for autonomous AI agents - guardrails, action auditing, permission boundaries, and kill switches. Prevent agents from taking unauthorized actions in production.
AI Security Operations Insights
Insights on AI-powered SOC, ML pipeline monitoring, and autonomous threat detection from the secops.qa team.
Why Your SIEM Can't Detect AI Threats: Building an AI-Native Security Operations Capability
Traditional SIEM and EDR tools were built to detect attacks against deterministic systems. AI systems fail in different …
Monitoring AI Agents in Production: A Security Operations Playbook
AI agents are the hardest AI systems to secure because their behavior is non-deterministic, their tool access creates …
ML Pipeline Security Monitoring: From Data Ingestion to Model Serving
The ML pipeline - from raw data ingestion through feature engineering, training, evaluation, and model serving - is a …
How We Deploy AI Security Operations
Five phases from assessment to continuous protection. We instrument your AI stack, establish behavioral baselines, and operate a live SOC for your ML workloads.
Assess
Inventory AI assets, map data flows, identify monitoring gaps. Baseline current security posture against AI SOC readiness criteria.
Instrument
Deploy lightweight sensors on ML pipelines, LLM APIs, and agent runtimes. Integrate with your existing SIEM and observability stack.
Detect
AI-specific detection rules fire on anomalous model behavior, prompt injection patterns, pipeline tampering, and LLM abuse signatures.
Respond
Automated response playbooks contain threats within seconds. Expert analysts investigate, validate, and escalate confirmed AI security incidents.
Harden
Monthly security posture reviews. Detection rule tuning. Threat intelligence updates. Continuous improvement of your AI security baseline.
Comprehensive AI Security Operations
From a one-time AI SOC Readiness Assessment to a fully managed AI-Powered SOC - we provide the security operations coverage your AI stack requires.
AI-Powered SOC
Security operations purpose-built for AI workloads - real-time monitoring, AI-specific detection, and expert analyst response.
Autonomous Detection & Response
ML-driven threat detection that learns your model behavior baselines and automatically responds to AI-specific attacks.
ML Pipeline Monitoring
Continuous security monitoring for ML training and inference pipelines - detect tampering, drift, and unauthorized access.
AI Agent Runtime Protection
Runtime security controls for autonomous AI agents - guardrails, action auditing, permission boundaries, and kill switches.
AI Incident Response
Emergency response for AI security incidents - model compromise, data breach, agent gone rogue. 4-hour SLA available.
AI Security Posture Management
Continuous visibility into your AI risk - asset inventory, risk scoring, policy enforcement, and compliance mapping.
Industries We Defend
We bring AI security operations expertise to sectors where AI failures have safety, financial, or national security consequences.
Autonomous Vehicles & Robotics
Monitor perception models, sensor fusion pipelines, and OTA update integrity for safety-critical autonomous systems.
SaaS & AI Platforms
Defend multi-tenant AI at scale - cross-tenant isolation monitoring, API abuse detection, and AI feature kill switches.
Government & Defense
Continuous monitoring for mission-critical AI with FedRAMP-aligned controls and NIST AI RMF compliance.
Works with Your ML Stack
We instrument and monitor AI workloads across all major ML platforms - no rip-and-replace, no new infrastructure required. Deploy in days, not months.
Supported ML Platforms
LLM & Agent Runtimes
Observability & SIEM
Free AI SOC Readiness Assessment
See where your AI defenses stand. Our AI SOC Readiness Assessment evaluates your current monitoring coverage, detection capabilities, and incident response readiness for AI-specific threats.
Assess Your AI SOC ReadinessAI Security Operations - Frequently Asked Questions
What is an AI-powered SOC and how does it differ from a traditional SOC?
A traditional SOC monitors networks, endpoints, and applications for threats like malware, intrusions, and data exfiltration. An AI-powered SOC adds coverage for threats specific to AI workloads - prompt injection attacks against LLMs, model behavior anomalies, ML pipeline tampering, LLM abuse by unauthorized users, and AI agent runaway scenarios. secops.qa's AI SOC combines AI-native detection rules with expert analysts who understand ML architectures, not just security operations playbooks.
What does ML pipeline monitoring cover?
Our ML pipeline monitoring covers the full ML lifecycle - training data integrity checks, model training job anomalies, model registry access auditing, inference API traffic analysis, model drift and performance degradation alerting, and post-deployment behavioral monitoring. We instrument pipelines on AWS SageMaker, Azure ML, GCP Vertex AI, Databricks, and self-hosted Kubernetes clusters. Detection latency is typically under 60 seconds for critical anomalies.
How does AI Agent Runtime Protection work?
AI agents are autonomous systems that can take real-world actions - sending emails, executing code, modifying databases, making API calls. Our runtime protection layer intercepts agent actions before execution, evaluates them against defined permission boundaries and risk policies, logs all actions to an immutable audit trail, and can automatically block or quarantine agents exhibiting anomalous behavior. We support LangChain, AutoGen, CrewAI, and custom agent frameworks via our lightweight SDK.
What is your SLA for AI Incident Response?
Our standard AI Incident Response SLA provides initial triage within 4 hours of incident declaration and a preliminary containment assessment within 8 hours. For retainer clients on our AI-Powered SOC service, we provide a 1-hour initial response SLA with 24/7 coverage. Emergency response engagements for non-retainer clients are available on request with a 4-hour response commitment. All engagements include a full post-incident report with root cause analysis and remediation recommendations.
How long does it take to deploy the AI SOC?
Most deployments are operational within 5–10 business days. Day 1–2 covers the AI SOC Readiness Assessment and instrumentation planning. Days 3–5 cover sensor deployment and SIEM integration. Days 6–8 cover baseline establishment and detection rule tuning. Days 9–10 cover go-live and analyst handoff. For complex multi-cloud environments, deployment may take 3–4 weeks. We do not require any existing SIEM infrastructure - we can deploy standalone or integrate with your existing stack.
Defend AI with AI
Start with a free AI SOC Readiness Assessment and see where your AI defenses stand.
Assess Your AI SOC Readiness