Page 85 - Cyber Defense eMagazine September 2025

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Understanding Agentic AI vs AI Agents

At its core, agentic AI consists of autonomous agents capable of reasoning, learning, and taking
independent action to achieve specific goals. Unlike traditional AI models that generate responses based
on prompts, agentic AI engages in iterative workflows, adapting to dynamic environments in real time.

A key distinction exists between agentic AI and AI agents. While AI agents perform individual tasks,
agentic AI represents a broader system of interacting agents capable of sophisticated decision-making
and self-improvement.

This shift marks a departure from conventional automation. Traditional AI solutions often rely on
predefined rules and models, while agentic AI can assess complex situations, weigh multiple factors, and
act with minimal human intervention. In cybersecurity, this capability is invaluable for responding to
evolving threats and helping with remediation.

The Benefits of Agentic AI in Cybersecurity

Enhanced Threat Detection and Response

Agentic AI accelerates cybersecurity operations by reducing the Mean Time to Detect (MTTD) and Mean
Time to Conclusion (MTTC). Through real-time anomaly detection and automated incident response,
agentic AI can swiftly identify and neutralize threats before they escalate. Lowering MTTD means
detecting threats faster, minimizing the time attackers have to exploit vulnerabilities, while reducing MTTC
ensures threats are mitigated swiftly before they cause major disruptions.

Moreover, agentic AI minimizes human error by automating repetitive tasks, allowing security
professionals to focus on complex investigations. By eliminating alert fatigue and prioritizing critical
threats, security teams can operate more effectively.

Scalability and Cost Efficiency

As cyber threats grow in volume and complexity, hiring additional security analysts becomes increasingly
difficult and expensive. Agentic AI provides a scalable solution by automating key security functions,
reducing the need for large Security Operations Center (SOC) teams while maintaining high levels of
protection.

How Security Will Evolve with Agentic AI

Traditional security solutions rely on human-led decision-making, often reacting to threats after they
occur. In contrast, agentic AI-driven security will shift from reactive to proactive threat management.

For example, current AI-driven security tools, such as Large Language Models (LLMs), primarily assist
in generating responses or analyzing data. With agentic AI, security tools will move toward self-sufficient
operations, autonomously managing cyber risks and adapting to new attack vectors. This shift will
redefine security as a service, transitioning from software as a service (SaaS) to service as a software.

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