Managed Detection and Response for AI Systems
24/7 AI-Aware Security Operations
Traditional MDR services monitor networks, endpoints, and cloud workloads — but they're blind to AI-specific attack vectors. Prompt injection, model manipulation, agentic workflow hijacking, and data poisoning all bypass conventional detection. Your AI systems need security operations designed for how they actually work.
Why Traditional MDR Falls Short for AI
Managed Detection and Response has become a cornerstone of modern security operations. But MDR services built for traditional infrastructure operate on assumptions that don't hold for AI systems: predictable inputs, deterministic behavior, and static attack surfaces.
AI systems are fundamentally different. Large language models produce variable outputs from identical inputs. Agentic workflows chain autonomous decisions across multiple systems with emergent behavior. The attack surface shifts with every prompt, every tool call, every model update. Standard SIEM rules and endpoint detection can't identify an adversarial prompt or a subtly poisoned training dataset.
AI-aware MDR extends traditional security monitoring with detection capabilities purpose-built for probabilistic systems — covering the gap between where conventional tools stop and where AI-specific threats begin.
Our MDR Approach
A structured methodology that integrates AI-specific detection into your existing security operations — not a replacement, but a critical extension.
AI Threat Landscape Assessment
Map your AI systems, their data flows, external interfaces, and autonomous capabilities. Identify which components are exposed, what tools agents can access, and where traditional monitoring has blind spots.
Detection Engineering
Build detection rules and behavioral baselines specific to your AI systems. This goes beyond signature matching — we develop heuristics for anomalous model behavior, unexpected tool usage patterns, and adversarial input indicators.
Continuous Monitoring
24/7 monitoring of AI system telemetry including model inputs and outputs, agent decision logs, tool invocations, and data pipeline integrity. Automated triage separates genuine threats from noise, reducing alert fatigue for your team.
Investigation and Analysis
When detections fire, analysts with AI security expertise investigate. They understand the difference between a failed prompt injection and a successful context poisoning attack — context that generalist SOC analysts typically lack.
Incident Response
AI-specific response playbooks for containment and remediation. When an agent is compromised, the response path differs from a traditional endpoint incident — you may need to quarantine memory, revoke tool access, or roll back model state rather than isolating a host.
Reporting and Tuning
Regular reporting on AI threat trends, detection efficacy, and coverage gaps. Detection rules evolve as your AI systems change — new model deployments, prompt modifications, and expanded agent capabilities all require updated monitoring.
AI-Specific Threats We Monitor
Prompt Injection Attempts
Both direct injection through user inputs and indirect injection through retrieved content, tool outputs, or data sources that influence LLM behavior. Includes multi-turn attacks that build influence across conversations.
Model Behavior Drift
Deviations from established behavioral baselines that may indicate model manipulation, fine-tuning attacks, or degraded safety alignments. Subtle drift can signal compromise before catastrophic failure occurs.
Data Exfiltration via AI APIs
Techniques that extract training data, system prompts, internal knowledge, or sensitive information through carefully crafted queries. Includes membership inference and model inversion attacks against your APIs.
Agentic Workflow Compromise
Goal hijacking, unauthorized tool usage, privilege escalation through agent capabilities, and cascading failures across multi-agent architectures. Autonomous systems require autonomous monitoring. (OWASP ASI01, ASI02)
Supply Chain Attacks
Compromised model weights, poisoned fine-tuning datasets, malicious dependencies in ML pipelines, and backdoored pre-trained models. The AI supply chain introduces risks that traditional SCA tools don't assess.
Adversarial Input Patterns
Inputs crafted to cause misclassification, bypass content filters, trigger unsafe behaviors, or exploit model vulnerabilities. These attacks are invisible to traditional input validation and WAF rules.
Traditional MDR vs. AI-Aware MDR
The Sentinel Nexus Approach
AI-aware MDR doesn't replace your existing security operations — it extends them. We integrate AI-specific detection and response capabilities with your current SIEM, SOAR, and MDR infrastructure, closing the visibility gap without duplicating coverage.
Our approach connects monitoring to the broader security lifecycle. Red teaming findings inform detection rules. Governance policies define what constitutes a violation. Implementation architecture determines what telemetry to collect. MDR becomes more effective when it's part of an integrated program.
Proactive Testing Informs Detection
Red teaming identifies the attack patterns your MDR should detect. Adversarial findings become detection rules, creating a feedback loop that strengthens both services.
Learn about AI Red Teaming →Governance Defines Boundaries
AI governance policies establish what your systems should and shouldn't do. MDR monitoring enforces those boundaries in production, alerting on policy violations and unauthorized behaviors.
Learn about AI Governance →Ready to protect your AI systems around the clock?
Let's discuss how AI-aware managed detection and response can close the security gaps in your AI infrastructure.
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