Why AI Security Tools Need Better Data, Not Just Better Models

Key points

• Global AI in cybersecurity market projected to grow from $44 billion in 2026 to $213 billion by 2034.
• Average enterprise runs 83 different security tools, leading to inconsistent data formats.
• Poor data quality costs organizations an average of $12.9 million annually.

Organizations are investing heavily in artificial intelligence (AI) for cybersecurity, with the market expected to soar from $44 billion in 2026 to $213 billion by 2034. This investment reflects a belief that AI can help manage the vast amount of security threats that human analysts alone cannot keep up with. However, when AI-driven tools underperform, the tendency is to adjust the algorithms or ask vendors for improvements. The real issue often lies with the data these tools rely on.

Many companies don’t have clean, unified security data. Instead, they're dealing with years of accumulated decisions, using an average of 83 different security products from 29 vendors. These tools produce their own data in various formats, making it hard for AI systems to find patterns. Unlike human analysts who can adapt to these inconsistencies, machine learning models struggle when the same data field is named differently across tools. This inconsistency in data is sometimes called “schema drift” and can make AI tools less effective.

Why is bad data such a problem?

Bad data doesn’t just make AI models less accurate; it can also be costly. IBM found that poor data quality costs organizations $12.9 million per year. In cybersecurity, this can lead to false alerts, missed real threats, and more work for analysts. As companies change and grow, their data changes too, but if the AI models aren't updated with fresh data, they might flag normal behavior as suspicious. This is especially true as businesses shift to hybrid work, adopt new cloud tools, or merge with other companies.

The disconnect between data management and cybersecurity is often structural. Data pipelines are managed by engineering teams, while detection models are managed by security teams. This division means no one is solely responsible for ensuring that AI systems receive consistent and high-quality data. Security leaders should focus on three areas: standardizing data formats, monitoring data quality, and applying rigorous governance to security data.

The AI security tools in use today have the potential to protect against modern threats. However, their effectiveness depends on the quality and consistency of the data they analyze. Before making further investments into AI models or platforms, organizations should ensure their data architecture supports these systems effectively.

This analysis first appeared on CSO Online.