Detect AI Threats at Machine Speed.
Behavioral baselines, 50+ AI-specific detection rules, and automated response playbooks - autonomous security for AI systems that moves faster than any human-only operation.
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Autonomous detection and response for AI systems solves the fundamental speed problem in AI security: adversarial attacks against LLM applications and AI agents can execute faster than human analysts can detect and respond. An automated prompt injection chain can exfiltrate sensitive data through model outputs in minutes. Autonomous response must match the automation of the attack.
The Speed Asymmetry Problem
Modern AI security threats are increasingly automated. Adversaries use scripts to probe LLM applications systematically - testing hundreds of prompt injection variants, enumerating model behaviors, and building attack chains that span multiple interactions. A skilled attacker with automated tooling can complete a full prompt injection campaign and data exfiltration sequence in the time it takes a human analyst to notice the anomaly in a SIEM dashboard.
Manual detection and response cannot win this race. The only effective counter to automated attacks is automated detection and response with human oversight for high-impact decisions.
Behavioral Baselining as the Foundation
Effective AI threat detection starts with knowing what normal looks like. Before we write detection rules, we observe your AI workloads in production for two weeks - documenting normal API call rates, typical prompt lengths and patterns, standard agent tool call sequences, expected inference latencies, and normal data access patterns.
These behavioral baselines are the foundation of effective detection. Rules tuned against your actual normal behavior generate dramatically fewer false positives than generic rules, and they catch the genuine anomalies that matter: a sudden spike in model API queries from a single source, an agent making tool calls outside its established behavioral envelope, inference patterns consistent with systematic model extraction.
50+ Detection Rules, Not Generic Signatures
Our AI detection rule library covers six threat categories with rules specifically designed for AI workload patterns - not repurposed network security rules or generic API security checks. Each rule is tuned to your behavioral baseline during deployment and refined monthly to reduce false positives and improve coverage as the threat landscape evolves.
The detection rule library is delivered as a documented, versioned asset your team can review, extend, and maintain. Security programs built on undocumented black-box detection are fragile; ours are transparent and maintainable.
Engagement Phases
Baseline
Log source onboarding, data pipeline integration, and behavioral baseline establishment. Normal patterns documented for model API usage, inference volumes, agent tool call frequencies, and data access patterns - creating the foundation for anomaly detection.
Rule Engineering
Custom detection rule development across 6 AI threat categories: prompt injection patterns, model enumeration indicators, excessive agency signals, data exfiltration via outputs, supply chain anomalies, and training pipeline integrity. 50+ rules deployed with tuned thresholds.
Deployment
SIEM integration, automated response playbook deployment, alert routing configuration, escalation workflow setup, and initial false positive tuning against your production environment.
Tuning
False positive analysis, threshold refinement, new rule development based on emerging threats, monthly detection effectiveness review, and continuous improvement against your evolving AI workload.
Deliverables
Before & After
| Metric | Before | After |
|---|---|---|
| Detection Coverage | Zero AI-specific detection rules | 50+ rules across 6 AI threat categories |
| Response Time | Manual detection and response - hours to days | Automated response triggered in seconds on confirmed events |
| False Positive Rate | Generic rules produce alert fatigue | Tuned AI-specific rules with monthly false positive reduction |
Tools We Use
Frequently Asked Questions
What AI threat categories do your detection rules cover?
Our detection rule library covers six AI threat categories: prompt injection (direct and indirect patterns in model API traffic), model enumeration (systematic querying patterns indicating extraction or reconnaissance), excessive agency (agent tool calls outside established behavioral baselines), output-based exfiltration (response patterns indicating sensitive data disclosure), supply chain anomalies (unexpected model update events or training pipeline access), and training pipeline integrity (unauthorized access to training data or model artifacts).
What SIEM platforms do you support?
We support Splunk, Elastic (formerly ELK), Microsoft Sentinel, and Google Chronicle as primary SIEM targets. Detection rules are developed in the query language native to your platform. For organizations without an existing SIEM, we can recommend and help deploy an appropriate solution as part of the setup phase.
How do automated response playbooks work?
Automated response playbooks define predefined actions triggered when a confirmed security event is detected. For a prompt injection detection, a playbook might automatically rate-limit the suspicious API client, capture the full session for forensic review, and notify the on-call analyst. For a model enumeration pattern, a playbook might temporarily block the source IP, log the extraction attempt for legal documentation, and trigger a model integrity check. Playbooks are reviewed with your team before deployment to ensure automated actions align with your operational requirements.
How long does it take to establish behavioral baselines?
Two weeks of production observation is sufficient to establish statistically stable baselines for most AI workloads. High-variability workloads - AI systems with significant daily or weekly traffic patterns - may require four weeks. Baselines are documented and versioned, so when your AI workload evolves (new features, model updates, increased usage), we update baselines without treating legitimate changes as attacks.
Can this work alongside our existing detection infrastructure?
Yes. The AI-specific detection rules and baselines complement your existing detection infrastructure - they don't replace it. We integrate with your existing SIEM and follow your alert routing conventions. AI security events appear in your existing alert queue with AI-specific context and enrichment, so your analysts can handle them using familiar workflows.
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