Implementing AI for Incident Response: Lessons from the Field

Organizations in different industries are using AI-powered incident response solutions. They face various challenges. Real-life case studies show the obstacles, strategies to overcome them, and lessons learned by early adopters. The examination provides insights into managing false positives, integrating AI with existing systems, addressing skill gaps, and handling resistance to automation.

Case Study 1: Financial Institution Struggling with False Positives

A global financial institution used an AI-powered SOAR platform to improve incident detection and response. The institution hoped the solution would make operations smoother and enhance threat protection. However, at the start, too many false alarms made things chaotic, causing operational issues and lowering trust in the system.

Challenges The AI system generated 10,000 alerts daily, with 80% being false positives. It wrongly identified big transactions from loyal clients as threats. False positives led to actions such as IP blocking and account freezes, causing service disruptions.

Solution

Algorithm optimization: Historical data fed back into models for better threat contextualization.

Human-AI collaboration: Manual review layer added for high-risk alerts.

Enhanced thresholds: Risk scoring parameters were tuned to prioritize meaningful anomalies.

Outcome

60% reduction in false positives

40% faster response time

70% fewer customer complaints

Source: IBM Security X-Force: False Positives and AI in Financial Sector

Case Study 2: Manufacturing Company Facing Legacy Integration Issues

The manufacturing company needed to protect its ICS and SCADA systems. However, their old tech stack couldn’t work with the new AI-powered IDS platform.

Challenges

Lack of API support

Proprietary data formats

Cost-prohibitive hardware replacements

Solution

Middleware deployed for data translation

Gradual rollout starting with non-critical systems

Simulated datasets trained AI to mimic legacy behavior

Outcome

Full visibility achieved without system replacement

Anomalies detected in real-time

Progressive modernization of infrastructure

Source: Darktrace: Integrating AI with Legacy Systems ??

Case Study 3: Healthcare Provider Facing a Skills Gap

A regional hospital used an AI-powered system to safeguard Electronic Health Records (EHR) from ransomware. But, the staff didn’t have expertise in AI security.

Challenges

Poor configuration of AI models

Inability to triage alerts

Inadequate vendor-provided training

Solution

Customized training programs with simulations

Simplified dashboards and automation

Temporary MSSP hired for support

Outcome

Staff trained in 3 months

Successfully mitigated ransomware

Hospital operations continued uninterrupted

Source: Deloitte Insights: Addressing the Cybersecurity Talent Gap ??

Case Study 4: Retail Chain Grappling with Resistance to Automation

A retail chain deployed AI to combat payment fraud. Internal pushback delayed rollout.

Challenges

Employees feared automation and job loss

Skepticism about AI’s accuracy

History of failed tech rollouts

Solution

Leadership engaged employees early

Pilot tested with measurable KPIs

Positioned AI as support, not a replacement

Outcome

50% boost in fraud detection

30% drop in false positives

Improved employee satisfaction and trust

Source: CrowdStrike: Overcoming Human Resistance in Cybersecurity Automation

General Lessons Learned:

AI requires continuous tuning

Middleware enables AI in legacy environments

Training bridges skill gaps

Communication reduces automation fear

Additional Sources for Further Reading:

1. IBM Case Studies: AI Implementation Challenges in Cybersecurity ??
2. Darktrace Whitepapers: AI for Incident Response in Legacy Systems ??
3. Deloitte Insights: Overcoming Skills Gaps in AI-Driven Security Operations ??
4. CrowdStrike Research: Employee Resistance in Cybersecurity Automation ??
5. Microsoft Security Blogs: Lessons from Early AI Adopters in Cybersecurity ??

The article was extracted from my book "Artificial Intelligence in Cybersecurity", To dive deeper into these insights and explore more use cases, click this link to get the book from Amazon https://www.amazon.com/dp/B0DZ2WKK1L