The AI-Driven SOC (R)evolution

The Challenge of Scale and Complexity

DXC Technology, a global IT services leader, manages one of the largest cybersecurity infrastructures in the world. With 125,000 employees across 70 countries and a vast attack surface, the company faces an immense challenge in monitoring, detecting, and responding to security threats in real time. The cybersecurity landscape is rapidly evolving, with increasingly sophisticated attacks driven by AI-powered threat actors. Traditional Security Operations Centers (SOCs) rely heavily on human analysts, who must sift through thousands of daily alerts. This results in alert fatigue, slower response times, and an inability to scale defense mechanisms efficiently.

Michael Baker, DXC’s Chief Information Security Officer, recognized the urgent need to modernize the SOC model. “When I think about thousands, millions of intelligent AI-driven attack simulations hitting the edge of your network, the only way to defend is with AI models of our own.” The goal was clear: leverage AI to transform SOC operations, enhance incident response times, and alleviate the burden on security teams.

Shortcomings of Legacy SOCs

Historically, SOCs depended on manual processes and rule-based threat detection. Analysts manually triaged incidents, often overwhelmed by high volumes of false positives. This model was inherently limited by:

• Alert Fatigue: Security teams received thousands of alerts daily, many of which were false positives, leading to inefficiencies and analyst burnout.
• Slow Incident Response: Traditional SOCs required human intervention at every stage of an investigation, resulting in high Mean Time to Detect (MTTD) and Mean Time to Respond (MTTR).
• Lack of Automation: Legacy tools provided limited orchestration, requiring security professionals to correlate data across multiple platforms manually.
• Scalability Issues: As digital footprints expanded, legacy SOCs struggled to process vast amounts of security data effectively.

With cyber threats becoming more advanced—such as AI-driven phishing, deepfake-enabled fraud, and automated attack simulations—the need for a smarter, more automated approach was imperative.

Implementing AI-Driven Security Operations

DXC implemented an AI-powered SOC strategy that integrates cutting-edge security technologies to enhance cyber defense. The transformation was centered on three core innovations:

1. AI-driven security analytics: DXC deployed AI models capable of analyzing vast amounts of security data in real time, identifying subtle anomalies that might indicate threats. These models continuously learn from historical attack patterns to improve detection accuracy and reduce the risk of false negatives.
2. Automated threat detection & response: Security Orchestration, Automation, and Response (SOAR) platforms were introduced to automate predictable threat responses. AI-powered agents now handle tasks such as isolating infected endpoints, blocking malicious IPs, and escalating critical threats to human analysts when necessary.
3. Human-AI collaboration: Instead of replacing analysts, AI augments their capabilities by automating repetitive tasks and providing contextual insights. This shift enables security teams to focus on complex investigations, strategic threat hunting, and proactive risk mitigation.
4. Predictive threat intelligence: DXC enhanced its SOC with AI-driven threat intelligence that anticipates potential attack vectors based on global cyber threat trends, enabling preemptive defenses against emerging threats.
5. Adaptive AI models: DXC's SOC utilizes adaptive AI models that continuously refine detection mechanisms based on real-world attack scenarios, ensuring the AI remains effective against evolving cyber threats.

The transition to AI-powered SOCs required a structured implementation roadmap. DXC deployed AI-enhanced monitoring tools in phases, starting with automating low-risk alerts before advancing to real-time incident response automation. This approach allowed teams to refine AI models progressively and ensure seamless integration with existing security frameworks. Additionally, DXC integrated AI-based anomaly detection to enhance early-stage threat detection, preventing attackers from establishing persistent access within corporate networks.

Measurable Impact and Results

Since deploying AI-driven SOCs, DXC has achieved significant operational improvements:

• Reduction in alert fatigue: Automated filtering of false positives has cut unnecessary alerts by over 60%, allowing analysts to focus on genuine threats.
• Faster incident response: AI-powered triage and automated remediation have reduced MTTD and MTTR by 50%, minimizing the window of vulnerability.
• Enhanced threat detection: AI models have improved detection accuracy, reducing false negatives and identifying sophisticated attacks before they escalate.
• Cost efficiency: Automation has optimized security operations, enabling DXC to handle a growing number of threats without a proportional increase in headcount.
• Improved proactive defense: AI-driven predictive analytics have enabled DXC to proactively mitigate risks before they become full-scale incidents, reducing system downtime and potential damages.

Baker envisions a future where AI enables fully autonomous SOCs. “We’re talking about fully automated vulnerability and patch management. That’s the only way we can restrict intelligent AI-driven attacks at scale.”

Lessons Learned and Future Outlook

DXC’s experience highlights several key takeaways for organizations looking to modernize their cybersecurity operations:

• AI is essential for scale: As attack surfaces grow, human-centric security models will no longer suffice. AI-powered automation is critical to maintaining cybersecurity resilience.
• Security teams must evolve: With AI handling routine tasks, analysts must shift towards strategic decision-making, deep-dive investigations, and proactive threat hunting.
• Governance is crucial: AI-driven security requires robust governance to ensure responsible automation, particularly around sensitive data handling. Establishing strict AI governance policies ensures that automation enhances security without introducing risks.
• Continuous AI training: AI security models must be continuously trained and tested against new attack techniques to remain effective. DXC has implemented a model refinement strategy where AI is regularly updated with the latest threat intelligence.
• Integration across platforms: For maximum efficiency, AI-driven security must integrate seamlessly with existing IT and cloud environments, ensuring a unified security approach across all digital assets.

DXC’s AI-powered SOCs represent the next evolution in cybersecurity, setting the stage for a future where AI-driven defense mechanisms outpace adversarial threats. The company’s proactive adoption of automation ensures it remains at the forefront of enterprise security, paving the way for fully autonomous cybersecurity operations in the years to come.

As AI capabilities continue to expand, DXC’s approach demonstrates that organizations must embrace AI not just as a tool for efficiency but as a fundamental requirement for defending against increasingly intelligent cyber threats. The journey toward a fully autonomous SOC is ongoing, but DXC’s implementation provides a clear roadmap for security leaders looking to build the next generation of cyber defenses.