While external cyber threats capture headlines, the rise of insider threats from within an organization is a growing concern. In 2023, the average cost of a data breach caused by an insider reached $4.90 million, 9.6% higher than the global average data breach cost of $4.45 million. To effectively combat this danger, integrating advanced analytics into data security software has become a critical and proactive defense strategy.

Understanding insider threats

Insider threats come from users who abuse authorized access to a company’s assets deliberately or accidentally. There are typically two types of insider threats: intentional (malicious) and unintentional. An unintentional insider threat could be caused by negligence or simply an accident. Intentional threats are actions that harm an organization for personal benefit or grievance.

Regardless of the intent, both types of insider threats can have severe consequences for businesses. Detecting and mitigating these risks quickly is crucial — breaches initiated by malicious insiders took nearly 308 days to resolve.

Need for fast detection

Traditional methods of detecting threats have trouble keeping up with evolving attacker tactics. Advanced threat detection analytics can help to provide a dynamic, proactive way to swiftly identify insider threats by scanning and analyzing data. Rapid detection is crucial due to its role in reducing financial losses, preserving reputation, minimizing data exposure, meeting compliance demands and ensuring operational continuity.

Power of advanced analytics

Fueled by machine learning and artificial intelligence, advanced analytics has revolutionized organizations’ approach to identifying and responding to insider threats. By dynamically assessing risk factors to identify potential risks across an entire system, advanced analytics can establish behavior benchmarks and identify potential threats through evolving patterns.

Behavioral analysis examines historical and real-time data to develop detailed user behavior profiles to differentiate authorized actions from suspicious ones. Contextual insights, derived from vast datasets, provide a comprehensive view of user interactions for early anomaly detection. User and entity behavior analytics (UEBA) tools use diverse data sources to identify unusual behaviors. In addition, automation and orchestration streamline threat detection workflows and response coordination, reducing investigation time.

Data protection against insider threats

In a cybersecurity landscape loaded with daily challenges, insider threats demand immediate attention. Advanced analytics integrated into data security software offers a robust solution. Prompt detection of insider threats is pivotal for an organization’s financial stability, reputation and operational resilience. By leveraging advanced analytics, organizations can strengthen their defenses, stay ahead of insider threats and safeguard their crown jewels: their data.

If you’re interested in learning more about how advanced analytics in IBM Security’s Guardium Data Protection tool can help you detect insider threats faster, check out our website.