Just as triathletes know that peak performance requires more than expensive gear, cybersecurity teams discover that AI success doesn’t depend on the tools they deploy and more with the data that makes them work
Cybersecurity junk food issues
Imagine a triathlete who doesn’t spend money on equipment, such as a carbon fiber bicycle, a hydrodynamic wetsuit, or a precision GPS watch. Despite premium gear, their performance suffers because they are fundamentally flawed in their foundations. Triathletes can even determine racial outcomes by viewing nutrition as the fourth discipline of training that can have a significant impact on performance.
Today’s Security Operations Centers (SOCSs) face similar issues. They invest heavily in AI-powered detection systems, automated response platforms, and machine learning analytics. This is equivalent to professional grade triathlon equipment. But they drive these sophisticated tools using legacy data feeds that lack the richness and context that modern AI models need to execute effectively.
Just as triathletes need to swim, cycling and run seamlessly tuning, SOC teams need to be excellent at detecting, investigating, and responding. But without the own “fourth discipline”, SOC analysts work with no communication sparse endpoint logs, fragmented alert streams, and data silos. It’s like trying to complete a triathlon fueled with just a bag of tips and beer. You can load up sugar and calories on race day to ensure the energy to make it happen, but it’s not a sustainable, long-term regimen that optimizes your body for the best performance.
The hidden costs of the legacy data diet
“We live through the first wave of the AI revolution, and so far, Spotlight has focused on models and applications,” said Greg Bell, Chief Strategy Officer at CoreLight. “That makes sense, because the impact on cyber defense is enormous. But I think it’s starting to see dawning’s perception that it’s being measured by the quality of the data ML and genai tools consume.”
This disconnect between advanced AI capabilities and outdated data infrastructure creates what security experts now call “data debt.”
Traditional security data is similar to a training diary of a triathlete, often filled with incomplete entries. “I ran today. I felt it was fine.” Provides basic information, but there are no solid metrics, environmental context, or performance correlations that allow for real improvement. Typically, a legacy data feed includes:
- Sparse Endpoint Log Capture that event but misses the context of the action.
- Alert-only feed It tells you something has happened but it’s not a complete story
- Siloed data sources It cannot be correlated between systems or periods
- Reactive indicator Activating only after injury is already done without a historical perspective
- Unstructured form Large-scale processing is required before AI models can analyze them
The enemy has already strengthened their performance
Defenders struggle with data on undernutrition in AI consumption, but attackers have optimized their approach with discipline for elite athletes. They are leveraging AI to create faster, cheaper and more accurately targeted adaptive attack strategies than before.
- Automating reconnaissance Use development to accelerate attack speed
- Reduce costs per attackIncreased potential threat amount Aster
- Personalizing your approach Based on AI-Goathed Intelligence to provide more targeted attacks
- Generate faster iterations Tactical improvements based on what is working
On the other hand, many SOCs are still trying to defend against these AI-enhanced threats using basic heart rate information, using data equivalent to training regimens in the 1990s, when competition uses comprehensive performance analytics, environmental sensors, and predictive modeling.
This creates an escalating performance gap. As attackers become more refined in using AI, the quality of defense data becomes increasingly important. Poor data does not only slow detection. It actively undermines the effectiveness of AI security tools and creates blind spots that sophisticated enemies can exploit.
AI-enabled data: Performance-enhanced SOC is required
This solution is a fundamental rethinking of the security data architecture for what the AI model actually needs to work effectively. This means moving from a legacy data feed to what is called “AI-Reaid” data, that is, moving to information that is configured, enriched and optimized for AI analysis and automation.
AI-Reaid data shares comprehensive performance metrics and characteristics that elite triathletes use to optimize their training. Just as these athletes track everything from output and cadence to environmental conditions and recovery markers, AI Ready security data captures not only what happened, but the complete context surrounding each event.
This includes network telemetry that provides visibility before encryption, blurred evidence, comprehensive metadata that reveals behavioral patterns, and structured forms that allow AI models to be processed immediately without extensive preprocessing. Data is specially designed to supply three key components of AI-powered security operations.
AI-driven threat detection Equipped with forensic grade network evidence, including full context and real-time collections across on-premises, hybrid, and multi-cloud environments, can be dramatically effective. This allows AI models to identify subtle patterns and anomalies that are not visible in traditional log formats.
AI Workflow Transform the analyst experience by providing an expert-written process 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 concrete and actionable guidance for improvement.
AI-enabled ecosystem integration Make AI Ready data seamlessly flow to existing SOC tools (SOAR platforms, XDR systems, and Data Lakes, including SIEMS, SOAR platforms, XDR systems, and Data Lakes, without the need for custom integration or format conversions. It is automatically compatible with almost every tool in the Analyst’s Armory.
Combined effects of excellent data
The impact of migrating to AI-enabled data creates a complex effect across security operations. Teams can correlate unusual access patterns with privilege escalations in short-lived cloud environments. This is important for dealing with cloud-native threats that traditional tools miss. They gain expanded coverage of new, avoidable, and zero-day threats while allowing for faster development of new detections.
Perhaps most importantly, analysts can quickly understand incident timelines without parsing raw logs, get a plain language summary of suspicious behavior across hosts and sessions, and focus their attention on priority alerts with clear legitimacy as to why each incident is important.
“High-quality, contextual data is the ‘clean fuel’ that AI needs to achieve its potential fully,” Bell added. “A model that is hungered for quality data is inevitably disappointing. When AI augmentation becomes the norm for both attack and defense, a successful organization becomes an organization that understands the fundamental truths. In the world of AI security, you are what you eat.”
The training decisions that every SOC must do
Once AI becomes the norm for both attack and defense, AI-driven security tools cannot reach their potential without the right data. Organizations that continue to supply legacy data to these systems may discover significant investments in next-generation technologies that are degraded against increasingly sophisticated threats. Those who are aware of this are not about exchanging existing security investments. It is to provide high quality fuel to provide their promises – they are in a position to unlock the competitive advantage of AI.
In an escalating battle with the threat of Ai-Enhanced, peak performance starts with something that really feeds the engine.
For more information about industry-standard security data models where all major LLMs are already trained, visit www.coreLight.com. CoreLight provides forensic grade telemetry to power SOC workflows, drive detection, and enable a wider SOC ecosystem.
