Leveraging AI for a Smarter, Proactive Approach to Cybersecurity Incident Management | Nasscom

In today's rapidly evolving digital landscape, cybersecurity incidents have become increasingly sophisticated. Companies across all industries are witnessing increasingly complex and diverse cyberattacks, insider threats and data exfiltration. Traditional data loss prevention (DLP) tools have served as the first line of defense, but they often struggle to distinguish real threats from routine activities. This leads to a flood of false alarms that overwhelm security teams. However, there is progress in AI-driven user entity and behavior analysis (UEBA) bring a new dimension to DLP incident management, enabling security professionals to focus on high-risk anomalies while reducing investigation fatigue.

Transition from a reactive to a proactive security posture

The traditional approach to detecting DLP incidents is largely reactive – alerts are generated after anomalous behavior occurs, leaving security teams struggling to determine whether the incident was actually malicious. A reactive posture also increases the breach lifecycle (time to identify and contain an incident), which is an average of 204 days for detection and an additional 73 days for containment, according to IBM's Cost of a Data Breach 2023 report.

AI offers an opportunity to flip the paradigm and transform DLP from a reactive to a predictive tool. By using machine learning algorithms, AI can monitor data flows and user activity and predict potential leaks before they occur. For example, an AI system could report a series of unusual data transfers or access attempts by an employee who does not normally interact with sensitive information, prompting an immediate investigation before any damage occurs.

The Role of AI in Proactive Threat Detection:

At the heart of the AI-powered UEBA solution is its ability to proactively detect threats by analyzing variations in user behavior. Traditional DLP systems rely on predefined rules and thresholds, which often results in an overwhelming amount of false positives. According to research from the Ponemon Institute, nearly 50% of security alerts are false alarms, resulting in wasted time and effort. Therefore, this represents a constant drain on resources as security teams detect innocuous incidents, leaving room for real risks to slip through. The value proposition of AI lies in its ability to reduce this noise and make the process much more efficient.

Cross-Channel Monitoring: A Holistic Approach to Incident Detection

Today, cybersecurity threats often span multiple vectors, making it essential for security systems to monitor user activity across different channels. Traditional DLP tools may struggle to correlate activity across these channels because they often lack the full context of an incident. However, DashMagiq's AI-powered system uses cross-channel analysis to detect suspicious activity that spans multiple platforms.

It analyzes user behavior across multiple channels – email, cloud storage, file transfers and more – the AI ​​system creates a detailed analysis Behavioral baseline for each user. This baseline helps distinguish between benign anomalies (e.g., a one-off, high-volume email) and real security risks (e.g., unauthorized, large-scale data transfers). This cross-channel monitoring allows AI to detect patterns that traditional tools may miss, especially in the case of insider threats or multi-stage exfiltration attempts.

For example, a user could download sensitive information to a USB device and then attempt to upload it to a third-party cloud service. Such a sequence could go unnoticed if only one channel were monitored, but AI's ability to correlate activity across multiple platforms ensures the full picture is captured.

Anomaly assessment and contextual insights for better investigations

One of the key features of this AI-driven solution is anomaly scoring. By assigning one Severity score For each DLP incident, security teams can prioritize their investigations to the most critical incidents based on the degree of deviation from established user policies. This scoring model takes into account the type of data involved, the sensitivity of the information, and the context of the user's role within the organization.

For example, if a user in the finance department attempted to transmit large amounts of highly sensitive data (e.g. confidential financial reports) to a personal email address, the system would assign a high anomaly score based on the deviation from their typical behavior. Contextual information Information such as the user's location, access history, and job title further enrich the investigation and enable teams to make faster, more informed decisions.

In addition to leveraging UEBA, DashMagiq aims to integrate other AI-driven dashboards that have the potential to not only visualize data but also make it actionable.

Future challenges and future of AI in cybersecurity

Although promising, integrating AI into cybersecurity systems is not without challenges. A key issue is ensuring the ethical use of AI, particularly with regard to user privacy. Organizations must establish clear data usage policies and implement robust monitoring systems to prevent misuse. Additionally, AI systems themselves can become the target of cyberattacks, particularly adversarial attacks in which threat actors attempt to manipulate AI algorithms.

However, AI is evolving and future developments will likely further refine these systems. As machine learning models become more capable of understanding complex behavioral patterns, the accuracy of anomaly detection will improve and reduce the likelihood of false positives. Additionally, innovations in contextual enrichment – ​​where AI systems draw on even more data sources – promise to provide even deeper insights into the risk profile of anomalies.

Integrating AI into incident resolution systems marks the beginning of a new era in cybersecurity – one in which proactive detection and streamlined investigations not only mitigate risk, but also enable security teams to act more accurately and securely. DashMagiq offers a comprehensive set of tools that not only detect threats, but also predict and prevent them.