Pierluigi Paganini
February 11, 2025
Artificial intelligence (AI) is transforming industries and redefining how organizations protect their data in today’s fast-paced digital world. With over 90% of enterprises storing at least some of their data in the cloud, AI’s ability to enhance security across complex, distributed environments is more vital than ever. While AI introduces certain risks, its power to proactively identify and close data security gaps offers unparalleled protection when applied effectively. By leveraging AI across key data layers, organizations can discover, classify, and safeguard sensitive information to stay ahead of evolving threats.
The Three-Layered Approach to Data Security
Effective data security platforms use a three-layered approach to address security challenges and deliver complete data protection. This framework helps organizations concentrate on critical aspects of data security: Discovery, Classification, and Protection. By leveraging AI within these layers, businesses can secure sensitive data more effectively and ensure stronger protection and proactive threat management. This structured approach aligns with industry insights, as 93% of security professionals cite AI as a crucial factor in enhancing cybersecurity efforts, reinforcing its significance in today’s data security landscape.
Discovery: Locating and Mapping Sensitive Data
The first critical step in data security is data discovery. AI helps organizations automate the discovery process at scale, especially in complex distributed and cloud environments. AI-powered tools can scan in real-time, identifying all files, databases, and repositories across a company’s entire data estate.
With AI, organizations gain a comprehensive view of their data posture, which includes mapping both active and inactive data sets—such as forgotten legacy systems or orphaned data. AI, together with other technologies, can help make scanning more efficient, by clustering data together so that greater scale can be achieved cost effectively.
Classification: Understanding and Categorizing Data
Once data is discovered, the next critical step is classification—but it’s not enough to simply classify data. Accurate data classification is essential to ensure sensitive information is protected based on its true level of sensitivity. AI enhances this process by understanding the context around data and then automatically tagging data with precise labels, such as public, confidential, or restricted, or even customer bespoke labels, and by applying regulatory classifications in line with compliance requirements.
Legacy data security tools often struggle with classification accuracy, relying on rigid rule-based systems (pattern matching based on regular expressions or dictionary-based classification) that produce inconsistent results. These approaches, while efficient, can miss critical nuances in data patterns, leading to misclassified information, unnecessary access restrictions, or exposure of sensitive data. AI-driven systems overcome these limitations by using advanced machine learning models and context-aware algorithms to recognize complex data types, providing a more reliable and dynamic classification framework. This is particularly useful for unstructured data (as found in most document stores, email and messaging systems, etc.)
By automating and refining the classification process, AI reduces the risk of mishandling data while maintaining compliance and operational efficiency.
Protection: Assessing Risks, Controlling Access, and Mitigating Threats
The final layer in the data security framework is protection. AI enhances security protection through proactive measures, constantly monitoring systems to ensure security protocols stay current and effective in addressing evolving threats.
AI plays a critical role in assessing and managing risk. AI tools can evaluate security configurations, identify vulnerabilities, and recommend prioritized actions for remediation. AI can also detect gaps in employee security practices, delivering targeted training to mitigate human errors and strengthen the organization’s security posture. Furthermore, related role-based access control systems restrict access to sensitive data, ensuring only authorized personnel can view or modify it.
In addition to assessing risks, AI can enhance threat detection by identifying unusual access patterns or anomalous behavior and flagging potential threats in real time. This allows security teams to act swiftly before any data breach can occur.
Key Takeaway: Leveraging AI for Comprehensive Data Security
Integrating AI across data security layers—Discovery, Classification, and Protection—enables organizations to effectively detect, classify, and secure sensitive data. This empowers businesses to stay ahead of potential threats in a rapidly changing digital landscape, ensuring comprehensive protection for their data and systems. By embracing AI as an enabler for enhanced data security, organizations can build a more resilient infrastructure, reduce vulnerabilities, and ensure data is adequately protected across its lifecycle.
About the author: David Stuart is Director of Product Marketing at Sentra. David has over 20 years of tech, product, leadership, and marketing management experience, focusing on cybersecurity, cloud, big data, and machine learning.
Follow me on Twitter: @securityaffairs and Facebook and Mastodon
(SecurityAffairs – hacking, Artificial intelligence)
