Orchestrated Autonomy in MDR How Agentic AI and Human Collaboration Build Resilient Cyber Defense

Embracing Orchestrated Autonomy in MDR

Managed Detection and Response (MDR) providers like eSentire are advancing what they call “orchestrated autonomy” to create a more resilient, adaptive defense system. This approach blends autonomous AI with human oversight, allowing security teams to respond to threats effectively without increasing headcount.

Introduction: Balancing Humans and AI in Security Operations

Security operations centers (SOCs) face a tough challenge: they must address more threats faster, often with fewer resources and zero tolerance for mistakes. This pressure is shifting the focus from basic automation to agentic AI — autonomous systems that take coordinated actions while staying under human control.

From Simple Triage to AI as Operational Partners

Traditional automation has helped with alert triage and repetitive tasks but often lacks flexibility and context awareness. eSentire’s MDR model pushes AI beyond these limits. Here, AI agents become active decision-makers embedded within the live response workflow.

These AI agents handle responsibilities once reserved for analysts, such as escalating alerts, suppressing false positives, and even initiating mitigation steps. They continuously learn from human feedback, allowing the system to improve over time. This approach doesn’t replace human analysts but augments their capabilities, letting them focus on critical decisions.

How Orchestrated Autonomy Works

Orchestrated autonomy rests on three key pillars:

• Telemetry Normalization: Consolidating data from multiple sources into a consistent format enables accurate analysis by both AI and human teams.
• Policy-Bound Actioning: AI agents operate within clear boundaries defined by customer policies, risk tolerance, and compliance rules, ensuring safe and expected responses.
• Continuous Feedback Loops: Analysts provide real-time feedback on AI decisions, driving ongoing system learning and improvement without expanding the team.

This model allows MDR teams to scale defense efforts responsibly and strategically. AI moves from reacting to incidents toward anticipating and improving defenses after each event.

Assessing AI Maturity in MDR Providers

Security leaders should evaluate MDR partners by their AI capabilities along this maturity curve:

• Stage 1: Rule-Based Automation — Basic scripts and playbooks with limited flexibility.
• Stage 2: Conditional Autonomy — AI can suggest or take actions within strict limits.
• Stage 3: Orchestrated Autonomy — AI agents and human analysts collaborate fluidly, guided by policies for real-time, context-aware decisions.

Providers operating at Stage 3, like eSentire, treat AI as a co-pilot — not just a tool — enabling stronger, more adaptive security operations.