Introduction
Centralized data stores, like SIEMs and data lakes, are excellent at aggregating information for detection, but they fall short when it comes to investigations. These tools weren’t designed with investigative workflows in mind, and trying to use them for this purpose often leads to inefficiencies that slow analysts down when time is critical.
Security analysts face a fundamental daily challenge: they're oversubscribed, constantly distracted by incoming alerts, messages, and cases, yet expected to focus on high-priority incidents that require deep investigation. The traditional approach forces them to become part-time security engineers, spending valuable time crafting complex queries instead of analyzing security threats.
Why centralized data stores struggle with investigations
SIEMs, security data lakes and similar platforms are optimized for alert generation, not dynamic exploration. Investigations require analysts to pivot quickly between data sources, uncover patterns, and drill down into specific areas—all tasks that centralized systems make unnecessarily complex.
Take SIEMs as an example. Analysts often need to write intricate queries in proprietary languages like KQL to retrieve relevant data. This process is cumbersome and error-prone, especially during live incidents where speed and accuracy are paramount. If an investigation reveals a false positive or narrows in scope early on, the analyst may have already wasted hours navigating a system that wasn’t built for the task.
Consider this common scenario: A Tier 1 analyst receives an alert about suspicious activity in AWS. Despite having Azure expertise, they now face hours of research followed by extensive consultations with system administrators just to retrieve basic data points. This creates a bottleneck where technical query knowledge becomes a prerequisite for effective security analysis.
When investigating a potential incident, analysts often need to:
- Learn specialized query languages for each data source
- Craft complex queries across multiple systems
- Manually retrieve and correlate data from disparate sources
- Communicate findings using inconsistent formats
These operational inefficiencies prevent analysts from focusing on their primary responsibility: determining whether security risks exist and what actions to take.
Another challenge is access to diverse data sources. For instance, investigating a GitHub Enterprise-related incident might require pulling logs from GitHub’s API—a task that could involve multiple steps and coordination with administrators. Centralized systems rarely streamline these workflows, leaving analysts stuck performing manual tasks that should be automated.
Analysts are overwhelmed by noise every day
Security operations centers (SOCs) are inundated with alerts, cases, and requests from leadership. Analysts often spend more time managing tools than solving problems. This fragmentation of attention leads to inefficiencies and missed opportunities to address high-impact threats.
For example, a Tier-1 analyst investigating a phishing campaign might spend hours piecing together email logs manually, cross-referencing them with identity data, and crafting reports for leadership—all while juggling other cases and incoming alerts. The result is wasted time on repetitive tasks instead of focusing on identifying root causes or assessing risk.
Using AI as an enabler: Supplementing analysts instead of replacing them
AI in security operations often comes with unrealistic expectations—promises of fully autonomous investigations or “magic” solutions that replace human expertise. The reality is far more practical: AI should supplement analysts by automating routine tasks and providing actionable insights that enhance decision-making.
At Command Zero, we’ve taken a pragmatic approach to AI implementation:
- Automation: Routine tasks like retrieving logs or scoping alerts are automated to save analysts time and reduce friction during investigations.
- Summarization: Large language models (LLMs) distill complex artifacts into concise summaries tailored to the analyst’s experience level—whether they’re Tier 1 or Tier 3 experts.
- Proactive Suggestions: The platform highlights overlooked areas or recommends next steps based on expert-driven best practices, ensuring investigations stay thorough without relying on analysts to ask the “perfect” questions upfront.
This implementation philosophy centers on supplementation rather than replacement. The goal isn't to remove analysts from the equation but to enhance their capabilities and efficiency.
Real-World applications: Faster insights and better communication
The impact of this approach is clear in real-world scenarios. Imagine an analyst investigating suspicious activity in AWS who isn’t familiar with its intricacies because they’ve primarily worked in Azure environments. Instead of spending hours learning AWS-specific nuances or crafting queries manually, Command Zero automates these processes and provides summarized findings tailored to their needs.
Even executives benefit from this streamlined workflow. Reports generated by the platform avoid technical jargon, making it easier for leadership to understand key findings without endless clarification cycles.
Moving Beyond Centralized Data Repositories
Centralized data repositories will always play a role in detection workflows, but they aren’t the answer for investigations. By combining automation with expert-driven AI capabilities, we can eliminate inefficiencies and empower analysts to focus on what matters most—mitigating risk and protecting the organization.
The future of security operations isn't about replacing human expertise with artificial intelligence. It's about creating symbiotic relationships where technology handles repetitive tasks while augmenting human decision-making capabilities.
As we continue developing these capabilities, our focus remains firmly on delivering practical value today rather than promising hypothetical benefits tomorrow. By anchoring our approach in pragmatic solutions to real-world challenges, we're helping security teams maximize their impact with existing resources.
The most effective AI implementations in security aren't those that attempt to replace analysts, but those that make analysts better at what they already do.
Book a demo with our team to see how Command Zero can complement your SIEM and data lake - supercharging tier-2+ analysis for your organization.
