Simply as triathletes know that peak efficiency requires greater than costly gear, cybersecurity groups are discovering that AI success relies upon much less on the instruments they deploy and extra on the info that powers them
The junk meals drawback in cybersecurity
Think about a triathlete who spares no expense on gear—carbon fiber bikes, hydrodynamic wetsuits, precision GPS watches—however fuels their coaching with processed snacks and power drinks. Regardless of the premium gear, their efficiency will undergo as a result of their basis is essentially flawed. Triathletes see vitamin because the fourth self-discipline of their coaching that may have a major influence on efficiency and might even decide race outcomes.
At the moment’s safety operations facilities (SOCs) face an identical situation. They’re investing closely in AI-powered detection methods, automated response platforms, and machine studying analytics—the equal of professional-grade triathlon gear. However they’re powering these subtle instruments with legacy information feeds that lack the richness and context trendy AI fashions have to carry out successfully.
Simply as a triathlete must grasp swimming, biking, and working in seamless coordination, SOC groups should excel at detection, investigation, and response. Nonetheless, with out their very own “fourth self-discipline,” SOC analysts might be working with sparse endpoint logs, fragmented alert streams, and information silos that do not talk, it is like making an attempt to finish a triathlon fueled solely by a bag of chips and a beer—regardless of how good your coaching or gear, you are not crossing the end line first. When you could load up on sugar and energy on race day to make sure you have the power to make it by means of, that isn’t a sustainable, long-term routine that may optimize your physique for the most effective efficiency.
The hidden value of legacy information diets
“We’re residing by means of the primary wave of an AI revolution, and up to now the highlight has targeted on fashions and functions,” mentioned Greg Bell, Corelight chief technique officer. “That is sensible, as a result of the impacts for cyber protection are going to be big. However I feel there’s beginning to be a dawning realization that ML and GenAI instruments are gated by the standard of information they devour.”
This disconnect between superior AI capabilities and outdated information infrastructure creates what safety professionals are actually calling “information debt”—the amassed value of constructing AI methods on foundations that weren’t designed for machine studying consumption.
Conventional safety information usually resembles a triathlete’s coaching diary full of incomplete entries: “Ran at present. Felt okay.” It supplies primary data however lacks the granular metrics, environmental context, and efficiency correlations that allow real enchancment. Legacy information feeds sometimes embrace:
- Sparse endpoint logs that seize occasions however miss the behavioral context
- Alert-only feeds that let you know one thing occurred however not the complete story
- Siloed information sources that may’t correlate throughout methods or time durations
- Reactive indicators that solely activate after harm is already executed with out historic views
- Unstructured codecs that require intensive processing earlier than AI fashions can analyze them
The adversary is already performance-enhanced
Whereas defenders wrestle with information that is nutritionally poor for AI consumption, attackers have optimized their strategy with the self-discipline of elite athletes. They’re leveraging AI to create adaptive assault methods which can be quicker, cheaper, and extra exactly focused than ever earlier than by:
- Automating reconnaissance and exploit improvement to speed up assault pace
- Decreasing the associated fee per assault, growing potential menace quantity aster
- Personalizing approaches based mostly on AI-gathered intelligence to ship extra focused assaults
- Producing faster iteration and enchancment of ways based mostly on what’s working
In the meantime, many SOCs are nonetheless making an attempt to defend in opposition to these AI-enhanced threats utilizing information equal to a Nineties coaching routine—with simply primary coronary heart charge data—when the competitors is utilizing complete efficiency analytics, environmental sensors, and predictive modeling.
This creates an escalating efficiency hole. As attackers turn into extra subtle of their use of AI, the standard of defensive information turns into more and more important. Poor information does not simply decelerate detection—it actively undermines the effectiveness of AI safety instruments, creating blind spots that subtle adversaries can exploit.
AI-ready information: the efficiency enhancement SOCs want
The answer lies in essentially reimagining safety information structure round what AI fashions really have to carry out successfully. This implies transitioning from legacy information feeds to what could possibly be known as “AI-ready” information—data that is structured, enriched, and optimized particularly for AI evaluation and automation.
AI-ready information shares traits with the great efficiency metrics that elite triathletes use to optimize their coaching. Simply as these athletes monitor all the pieces from energy output and cadence to environmental situations and restoration markers, AI-ready safety information captures not simply what occurred, however the full context surrounding every occasion.
This contains community telemetry that gives visibility earlier than encryption obscures the proof, complete metadata that reveals behavioral patterns, and structured codecs that AI fashions can instantly course of with out intensive preprocessing. It is information that is been particularly designed to feed the three important elements of AI-powered safety operations.
AI-driven menace detection turns into dramatically more practical when powered by forensic-grade community proof that features full context and real-time assortment throughout on-premise, hybrid, and multi-cloud environments. This allows AI fashions to establish delicate patterns and anomalies that might be invisible in conventional log codecs.
AI workflows rework the analyst expertise by offering expert-authored processes enhanced with AI-driven payload evaluation, historic context, and session-level summaries. That is equal to having a world-class coach who can immediately analyze efficiency information and supply particular, actionable steering for enchancment.
AI-enabled ecosystem integrations be certain that AI-ready information flows seamlessly into current SOC instruments—SIEMs, SOAR platforms, XDR methods, and information lakes—with out requiring customized integrations or format conversions. It is routinely appropriate with almost each device in an analyst’s arsenal.
The compound impact of superior information
The influence of transitioning to AI-ready information creates a compound impact throughout safety operations. Groups can correlate uncommon entry patterns and privilege escalations in ephemeral cloud environments, important for addressing cloud-native threats that conventional instruments miss. They achieve expanded protection for novel, evasive, and zero-day threats whereas enabling quicker improvement of latest detections.
Maybe most significantly, analysts can rapidly perceive incident timelines with out parsing uncooked logs, get plain-language summaries of suspicious behaviors throughout hosts and classes, and focus their consideration on precedence alerts with clear justifications for why every incident issues.
“Prime quality, context-rich information is the ‘clear gas’ AI wants to realize its full potential,” added Bell. “Fashions starved of high quality information will inevitably disappoint. As AI augmentation turns into the usual for each assault and protection, organizations that succeed would be the ones that perceive a elementary reality: on the planet of AI safety, you might be what you eat.”
The coaching resolution each SOC should make
As AI turns into normal for each assault and protection, AI-driven safety instruments can’t attain their potential with out the correct information. Organizations that proceed feeding these methods with legacy information could discover their important funding in next-generation know-how underperforming in opposition to more and more superior threats. People who acknowledge this is not about changing current safety investments — it is about offering them with the high-quality gas to ship on their promise — might be positioned to unlock AI’s aggressive benefit.
Within the escalating battle in opposition to AI-enhanced threats, peak efficiency really begins with what you feed your engine.
For extra details about industry-standard safety information fashions that each one the key LLMs have already been educated on, go to www.corelight.com. Corelight delivers forensic-grade telemetry to energy SOC workflows, drive detection, and allow the broader SOC ecosystem.
