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Agentic AI in Cybersecurity: A Primer Guide for Cybersecurity Architects

Publisher IDC
Published Jun 09, 2025
Length 17 Pages
SKU # IDC20118474

Description

This IDC Perspective on agentic AI in cybersecurity explores the evolution of AI in cybersecurity, emphasizing the role of agentic AI, which enables systems to exhibit agency through perception, reasoning, and action. The document outlines the progression from manual operations to dynamic autonomy, highlighting the importance of simplicity, security platforms, and standard IT architecture in achieving autonomy. It also discusses the integration of trustworthy AI elements, such as explainability and fairness, to ensure safe and ethical AI deployment in cybersecurity."Agentic AI platforms begin to iterate and adjust to changes in the security environment as reasoning engines become increasingly sophisticated and the compensating measures for probabilistic decision-making improve. AI agents handle most menial and repetitive SOC processes, from detection and investigation to response and remediation, with minimal human intervention, further reducing MTTI and MTTR. A feedback loop enables AI agents to create new detections and responses based on previously seen threats. Humans play a strategic role, overseeing the platform's adaptation to emerging threats and adjusting to changes in the IT architecture while maintaining resilience. A smart way to think of this is putting the 'human on the loop' as opposed to putting the 'human in the loop'." — Frank Dickson, group vice president, Security and Trust, IDC

Table of Contents

17 Pages

Executive Snapshot

Situation Overview

The Progression to Agentic AI in Cybersecurity

Advice for the Technology Buyer

Knowing the Difference Between Agentic AI Versus Copilot Use Cases

Planning for Your Agentic AI Approach

Agentic AI Progression

Elements of Trustworthy AI

Explainability — The Ability of an Agentic AI System to Articulate the Reasoning Behind Its Decisions in a Way That Humans Can Understand

Why It Matters

Questions to Ask

Fairness — Ensuring That Agentic AI Systems Do Not Produce Biased or Discriminatory Outcomes Across Different Demographic or Social Groups

Why It Matters

Questions to Ask

Transparency — Openness About How the Agentic AI System Is Built, Trained, and Deployed, Including Data Sources, Model Architecture, and Decision-Making Processes

Why It Matters

Questions to Ask

Accurate and Appropriate — Ensuring the Agentic AI System Performs to the Functional Purpose and Is Used Within the Scope It Was Designed for

Why It Matters

Questions to Ask

Provenance and Lineage — Tracking the Origin and Evolution of Data, Models, and Decisions Throughout the AI Life Cycle

Why It Matters

Questions to Ask

Adversarial Robustness — The Ability of an Agentic AI System to Resist Manipulation or Attacks Designed to Fool It

Why It Matters

Questions to Ask

Integrating These into an Agentic AI Strategy

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Related Research

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