Why case management is the ideal first step for using AI in your Security Operations Center. Learn how AI can augment analysts, reduce alert fatigue, and build organisational AI maturity without going full agentic.
Why Case Management is the Smartest Place to Trial AI in the SOC
The cybersecurity industry has a problem with shiny objects. Walk the floors of RSA 2025, and you are bombarded with AI promises that sound too good to be true - because they usually are. Vendors are pushing "fully autonomous" security operations, "lights-out" SOCs, and other marketing fantasies that ignore a fundamental truth: most organizations aren't ready to hand over their security keys to an algorithm.
But here's what the hype misses: you don't need to go full agentic to get real value from AI in Security Operations - especially when applied thoughtfully in your security operations center. The smartest approach isn't to replace human judgment - it's to augment it. And the best place to start? Case management.
The AI Hype at RSA: What Most SOCs Are Missing
RSA 2025 showcased an industry drunk on AI possibilities but light on practical implementation paths. Booth after booth promised revolutionary automation that would eliminate the need for human analysts entirely. The problem? These solutions require a level of trust and organizational maturity that most SOCs simply don't have yet.
The disconnect is stark. While vendors tout autonomous incident response and self-healing security architectures, real-world SOC managers are still struggling with basic challenges: alert fatigue, talent shortages, and the need to justify every security decision to business stakeholders who understand risk in dollars, not CVSS scores.
This gap between AI marketing and operational reality creates a dangerous temptation to either go all-in on unproven technology or dismiss AI in Security Operations entirely. Both approaches miss the mark.
Case Management: Your AI Training Ground
Case management represents the sweet spot for AI experimentation in security operations. Unlike real-time threat detection or automated response actions, case management provides a structured, controlled environment where AI can prove its worth without putting your organization at risk.
Think about what happens in case management: analysts respond to alerts, enrich observables, document findings and response actions, and make tactical decisions - often under significant time pressure. This environment presents a perfect testing ground for AI in Security Operations because it contains a mix of deterministic and non-deterministic tasks that can be strategically automated or augmented.
Deterministic tasks like enriching IP addresses, domains, and file hashes with threat intelligence are prime candidates for full automation. These repetitive, rule-based activities can be handled by traditional automation to achieve speed and reduce cognitive load on analysts. When an automated enrichment system pulls threat intelligence for an observable, there's little ambiguity about what constitutes success.
Non-deterministic tasks require human judgment, but a subset of these are perfect for LLM assistance under human supervision. Consider activities like drafting case summaries, explaining the significance of an observable in context, crafting communications for external stakeholders, building SIEM queries, constructing database searches, or even suggesting next steps in an investigation. These tasks benefit from the LLM's ability to synthesize information and generate coherent outputs, while preserving human agency and maintaining accountability.
The key is supervised LLM usage - the AI suggests, the human decides.This principle is at the heart of effective AI-driven case management strategies.This approach achieves speed and reduces cognitive burden while ensuring that critical security decisions remain under human control. If an LLM drafts a subpar case summary, an analyst reviews and corrects it. If an automated system makes an incorrect containment or blocking decision, your business operations could be severely impacted.
Case management also generates rich datasets that AI systems can learn from. Every closed case becomes training data, every analyst decision becomes a learning opportunity. This creates a virtuous cycle where AI gets smarter as your team uses it more, building confidence and capability over time.
Augmentation Over Automation: What Works in Practice
The most successful SOC AI in Security Operations implementations focus on making human analysts more effective, not replacing them. In case management, this means AI can excel at tasks like:
Evidence aggregation and correlation - AI can quickly pull together relevant logs, alerts, and context from multiple sources, presenting analysts with a comprehensive view faster than manual investigation allows.
Pattern recognition and similarity matching - when a new case arrives, AI can instantly identify similar historical incidents, suggesting investigation paths and potential outcomes based on past experience.
Documentation and reporting - AI can generate initial case summaries and draft SITREPs and preliminary reports that analysts can review and refine.
Notice what's missing from this list? Autonomous decision-making about security incidents. The AI isn't deciding whether something is malicious or benign, whether to escalate a case, or what response actions to take - it's giving human analysts better information and suggestions to make those decisions themselves.
This distinction becomes even more critical when considering response activities. While LLMs can draft containment procedures, suggest remediation steps, or help draft communications, the actual decision to isolate systems, block network traffic, or initiate emergency procedures must remain with human operators who understand business context, operational dependencies, acceptable risk levels, and stakeholder relationships. The speed advantage of AI assistance in drafting response plans can be significant, but the accountability for response decisions cannot be abrogated to an algorithm.
Building Organizational AI Maturity
Starting with case management creates something more than just improved incident handling - it builds organizational AI literacy. It also serves as a foundation for applying AI to security workflows in a measurable, iterative way. As your team works alongside AI in case management, they develop intuition about where AI excels and where it struggles. They learn to calibrate their trust appropriately, neither over-relying on AI insights nor dismissing them unnecessarily.
This experience becomes invaluable when you eventually expand AI into more critical areas of security operations. Analysts who understand AI strengths and limitations from case management experience are better equipped to oversee AI in threat detection, vulnerability management, or even limited response automation.
The goal isn't to create AI believers or skeptics - it's to develop AI-literate security professionals who can effectively collaborate with intelligent systems.
The Path Forward: Experiment, Measure, Evolve
The cybersecurity industry's AI future won't be built by vendors making bold promises at trade shows. It'll be built by security teams running careful experiments, measuring results, and evolving their practices based on real-world evidence.
Case management provides the perfect lab for this evolution. It offers meaningful work for AI systems without catastrophic failure modes. It generates measurable outcomes that can guide future AI investments. Most importantly, it builds the human-AI collaboration skills that will define the next generation of security operations.
So while the industry chases fully autonomous security operations, consider taking a different path. Start with case management. Focus on augmentation over automation. Define clear acceptability criteria. Build AI literacy within your team.
You don't need to go full agentic to transform your SOC. You just need to be smart about where and how you begin. Case management isn't just a good starting point - it might be the difference between AI success and another expensive lesson in cybersecurity hype.
The future of security operations will indeed be powered by AI. But it will be guided by human judgment, built on practical experience, and grounded in measurable results.