Ethical Hacking News
As AI-powered threats continue to evolve, cybersecurity teams are discovering that the quality of their data feeds is the key to unlocking the full potential of these advanced technologies. By recognizing the importance of high-quality data and adopting industry-standard security models, organizations can enhance their defenses against increasingly sophisticated attacks.
The quality of data powering AI-powered security tools is inadequate. Sparse and siloed data sources hinder the effectiveness of advanced security systems. Unstructured formats require extensive processing, making it challenging for AI models to analyze them effectively. Cybersecurity teams are struggling to keep pace with the latest advancements in AI-powered detection systems and automated response platforms.
The cybersecurity landscape is rapidly evolving, with threat actors increasingly leveraging Artificial Intelligence (AI) to enhance their attack strategies. Meanwhile, security teams are struggling to keep pace with the latest advancements in AI-powered detection systems, automated response platforms, and machine learning analytics. The main culprit behind this disparity lies not with the cutting-edge technology itself but with the data that powers it.
According to various sources, including cybersecurity experts and organizations specializing in threat intelligence, the quality of the data fed into these advanced security tools is woefully inadequate. For instance, sparse endpoint logs fail to capture events without providing any behavioral context, while alert-only feeds only notify users about something happening rather than conveying the full story. Siloed data sources that cannot correlate across systems or time periods exacerbate the issue, as do reactive indicators that only activate after damage has already been done.
Moreover, unstructured formats require extensive processing before AI models can analyze them effectively. This is where things become particularly concerning for cybersecurity teams. With many attackers now employing AI to optimize their approach, they are able to automate reconnaissance and exploit development at an accelerated rate, significantly reduce the cost per attack, personalize their approaches based on AI-gathered intelligence, and generate quicker iteration and improvement of tactics.
In contrast, security operations centers (SOCs) are often struggling with legacy data feeds that lack the richness and context required by modern AI models. This is akin to investing in premium gear for triathletes but fueling their training with processed snacks and energy drinks. The foundation of performance remains fundamentally flawed, which ultimately hampers the effectiveness of these high-end security tools.
Greg Bell, Corelight chief strategy officer, highlighted this issue when stating that Machine Learning (ML) and Generalized Artificial Intelligence (GenAI) tools are "gated by the quality of data they consume." This realization is now beginning to gain traction among cybersecurity professionals, who have started referring to the accumulated cost of building AI systems on foundations not designed for machine learning consumption as "data debt."
Traditional security data often resembles a triathlete's training diary filled with incomplete entries. It provides basic information but lacks granular metrics, environmental context, and performance correlations necessary for genuine improvement. Legacy data feeds typically include sparse endpoint logs, fragmented alert streams, and data silos that fail to communicate effectively.
The hidden cost of this legacy data diet can be dire. Without the right data, even the most advanced security tools may struggle to provide optimal protection against AI-enhanced threats. It is imperative for cybersecurity teams to recognize that their AI security tools' performance hinges on the quality of the data they are fed. By prioritizing high-quality data feeds and adopting industry-standard security data models already trained on by major LLMs, organizations can unlock the full potential of these advanced technologies.
Furthermore, Corelight delivers forensic-grade telemetry designed to power SOC workflows, drive detection, and enable the broader SOC ecosystem. For more information about how you can improve your cybersecurity with high-quality data, visit their website at www.corelight.com.
Related Information:
https://www.ethicalhackingnews.com/articles/The-Data-Paradox-How-Legacy-Data-is-Limiting-AIs-Potential-in-Cybersecurity-ehn.shtml
https://thehackernews.com/2025/08/you-are-what-you-eat-why-your-ai.html
Published: Fri Aug 1 06:57:18 2025 by llama3.2 3B Q4_K_M
