Page 54 - Cyber Defense eMagazine September 2025

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escalate them accordingly. This shift allows IR teams to focus their efforts on high-priority incidents while
automated systems handle low-level alerts.

Real-Time Incident Remediation

Historically, remediation efforts in response to an incident have been time-consuming, involving multiple
manual steps to isolate affected systems and mitigate the damage. Autonomous agents can carry out
initial remediation tasks such as disconnecting infected systems, blocking malicious actors, or initiating
network-wide scans automatically. These agents can respond to incidents faster than human analysts,
minimizing the window of exposure and reducing the impact of the attack.

Enhanced Collaboration and Communication

Communication across teams during an incident is crucial, but manual coordination can lead to delays.
Autonomous agents can automate communication between different stakeholders, including incident
responders, IT teams, and management, providing real-time updates and instructions. For example, once
an agent detects a threat, it can automatically notify the relevant teams and initiate response procedures,
ensuring swift and synchronized action.

Post-Incident Analysis and Reporting

Following an incident, autonomous agents can assist in post-incident analysis by providing detailed
reports on the attack timeline, affected systems, and response actions. These agents can also analyze
the attack vector and suggest improvements to security protocols, helping teams strengthen their
defenses for the future.

B. Maintaining Human Oversight
While autonomous agents significantly enhance incident response capabilities, human oversight remains
crucial. Human analysts are still needed to:

• Provide context: Autonomous agents can detect and respond to threats, but humans are needed
to interpret the broader context, such as understanding business priorities and risk tolerance.
• Make high-level decisions: In complex incidents, such as advanced persistent threats (APTs),
human decision-making is crucial for determining strategic responses and coordinating with
external partners, such as law enforcement or third-party vendors.
• Refine AI models: The effectiveness of AI-driven agents depends on continuous training and
refinement. Security teams must ensure that their AI models are kept up-to-date with emerging
threats and trends.

The key is finding the right balance between automation and human expertise. By automating routine
tasks and initial responses, autonomous agents can free up human analysts to focus on more strategic
aspects of incident response, such as threat hunting and long-term mitigation planning.

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