Just as triathletes know that peak performance requires more than expensive gear, cybersecurity teams are discovering that AI success depends less on the tools they deploy and more on the data that powers them
The junk food problem in cybersecurity
Imagine a triathlete who spares no expense on equipment—carbon fiber bikes, hydrodynamic wetsuits, precision GPS watches—but fuels their training with processed snacks and energy drinks. Despite the premium gear, their performance will suffer because their foundation is fundamentally flawed. Triathletes see nutrition as the fourth discipline of their training that can have a significant impact on performance and can even determine race outcomes.
Today’s security operations centers (SOCs) face a similar issue. They’re investing heavily in AI-powered detection systems, automated response platforms, and machine learning analytics—the equivalent of professional-grade triathlon equipment. But they’re powering these sophisticated tools with legacy data feeds that lack the richness and context modern AI models need to perform effectively.
Just as a triathlete needs to master swimming, cycling, and running in seamless coordination, SOC teams must excel at detection, investigation, and response. However, without their own “fourth discipline,” SOC analysts will be working with sparse endpoint logs, fragmented alert streams, and data silos that don’t communicate, it’s like trying to complete a triathlon fueled only by a bag of chips and a beer—no matter how good your training or equipment, you’re not crossing the finish line first. While you may load up on sugar and calories on race day to ensure you have the energy to make it through, that isn’t a sustainable, long-term regimen that will optimize your body for the best performance.
The hidden cost of legacy data diets
“We’re living through the first wave of an AI revolution, and so far the spotlight has focused on models and applications,” said Greg Bell, Corelight chief strategy officer. “That makes sense, because the impacts for cyber defense are going to be huge. But I think there’s starting to be a dawning realization that ML and GenAI tools are gated by the quality of data they consume.”
This disconnect between advanced AI capabilities and outdated data infrastructure creates what security professionals are now calling “data debt”—the accumulated cost of building AI systems on foundations that weren’t designed for machine learning consumption.
Traditional security data often resembles a triathlete’s training diary filled with incomplete entries: “Ran today. Felt okay.” It provides basic information but lacks the granular metrics, environmental context, and performance correlations that enable genuine improvement. Legacy data feeds typically include:
- Sparse endpoint logs that capture events but miss the behavioral context
- Alert-only feeds that tell you something happened but not the full story
- Siloed data sources that can’t correlate across systems or time periods
- Reactive indicators that only activate after damage is already done without historical perspectives
- Unstructured formats that require extensive processing before AI models can analyze them
The adversary is already performance-enhanced
While defenders struggle with data that’s nutritionally deficient for AI consumption, attackers have optimized their approach with the discipline of elite athletes. They’re leveraging AI to create adaptive attack strategies that are faster, cheaper, and more precisely targeted than ever before by:
- Automating reconnaissance and exploit development to accelerate attack speed
- Reducing the cost per attack, increasing potential threat volume aster
- Personalizing approaches based on AI-gathered intelligence to deliver more targeted attacks
- Generating quicker iteration and improvement of tactics based on what is working
Meanwhile, many SOCs are still trying to defend against these AI-enhanced threats using data equivalent to a 1990s training regimen—with just basic heart rate information—when the competition is using comprehensive performance analytics, environmental sensors, and predictive modeling.
This creates an escalating performance gap. As attackers become more sophisticated in their use of AI, the quality of defensive data becomes increasingly critical. Poor data doesn’t just slow down detection—it actively undermines the effectiveness of AI security tools, creating blind spots that sophisticated adversaries can exploit.
AI-ready data: the performance enhancement SOCs need
The solution lies in fundamentally reimagining security data architecture around what AI models actually need to perform effectively. This means transitioning from legacy data feeds to what could be called “AI-ready” data—information that’s structured, enriched, and optimized specifically for AI analysis and automation.
AI-ready data shares characteristics with the comprehensive performance metrics that elite triathletes use to optimize their training. Just as these athletes track everything from power output and cadence to environmental conditions and recovery markers, AI-ready security data captures not just what happened, but the full context surrounding each event.
This includes network telemetry that provides visibility before encryption obscures the evidence, comprehensive metadata that reveals behavioral patterns, and structured formats that AI models can immediately process without extensive preprocessing. It’s data that’s been specifically designed to feed the three critical components of AI-powered security operations.
AI-driven threat detection becomes dramatically more effective when powered by forensic-grade network evidence that includes full context and real-time collection across on-premise, hybrid, and multi-cloud environments. This enables AI models to identify subtle patterns and anomalies that would be invisible in traditional log formats.
AI workflows transform the analyst experience by providing expert-authored processes enhanced with AI-driven payload analysis, historical context, and session-level summaries. This is equivalent to having a world-class coach who can instantly analyze performance data and provide specific, actionable guidance for improvement.
AI-enabled ecosystem integrations ensure that AI-ready data flows seamlessly into existing SOC tools—SIEMs, SOAR platforms, XDR systems, and data lakes—without requiring custom integrations or format conversions. It’s automatically compatible with nearly every tool in an analyst’s arsenal.
The compound effect of superior data
The impact of transitioning to AI-ready data creates a compound effect across security operations. Teams can correlate unusual access patterns and privilege escalations in ephemeral cloud environments, critical for addressing cloud-native threats that traditional tools miss. They gain expanded coverage for novel, evasive, and zero-day threats while enabling faster development of new detections.
Perhaps most importantly, analysts can quickly understand incident timelines without parsing raw logs, get plain-language summaries of suspicious behaviors across hosts and sessions, and focus their attention on priority alerts with clear justifications for why each incident matters.
“High quality, context-rich data is the ‘clean fuel’ AI needs to achieve its full potential,” added Bell. “Models starved of quality data will inevitably disappoint. As AI augmentation becomes the standard for both attack and defense, organizations that succeed will be the ones that understand a fundamental truth: in the world of AI security, you are what you eat.”
The training decision every SOC must make
As AI becomes standard for both attack and defense, AI-driven security tools can’t reach their potential without the right data. Organizations that continue feeding these systems with legacy data may find their significant investment in next-generation technology underperforming against increasingly advanced threats. Those that recognize this isn’t about replacing existing security investments — it’s about providing them with the high-quality fuel to deliver on their promise — will be positioned to unlock AI’s competitive advantage.
In the escalating battle against AI-enhanced threats, peak performance truly begins with what you feed your engine.
For more information about industry-standard security data models that all the major LLMs have already been trained on, visit www.corelight.com. Corelight delivers forensic-grade telemetry to power SOC workflows, drive detection, and enable the broader SOC ecosystem.