Behavioral Analytics and UEBA: Key Tools Against Insider Threats

Insider threats remain among the costliest security risks. In fact, a recent Ponemon study finds the average annual cost of insider incidents has climbed to $17.4M up from $16.2M in 2023. However, organizations are getting quicker at detection and containment. The time to contain insider incidents has dropped to 81 days in 2024 (from 86 days in 2023). These trends underscore both the challenge and progress as companies that are investing more in insider risk tools are seeing measurable gains in response time and loss reduction.

Behavioral Analytics and UBA: How They Work

Behavioral Analytics, often realized today as User and Entity Behavior Analytics (UEBA), is the practice of baseline monitoring user and system activities focused on detecting threats by analyzing behavior of users and entities. Instead of relying on static rules, UEBA uses machine learning to establish “normal” patterns (for users, endpoints, applications, etc.) and flags deviations. For example, unusual access patterns (a user suddenly browsing unfamiliar files or logging in at odd hours) or large data transfers (sending voluminous files after hours) are red flags. Behavioral tools automatically correlate events such as multiple failed logins, privilege escalations outside normal roles, and the use of removable drives or cloud storage for sensitive data, all of which feed into risk scoring. By continuously profiling each user and entity (such as devices, processes, applications), UEBA can uncover insider activities that signature-based tools would miss. In short, behavioral analytics makes the human factor visible to security operations in real time.

UEBA is becoming a standard part of the security stack. A recent survey found that 51% of organizations have deployed user and entity behavior analytics tools to fight insider threats. These tools work in concert with other controls such as Network Detection & Response (NDR) systems that incorporate UEBA to watch network traffic for anomalies, while Data Loss Prevention (DLP) platforms may use user-behavior context to decide when to block or transfer. Combined, these technologies create layered visibility that span endpoints, networks, and cloud services.

Discover & Prioritizing Insider Risks

Behavioral analytics excels at discovering potential insider threats early. By continuously monitoring thousands of actions per user, it can detect subtle risk indicators long before exfiltration occurs. For example, if an employee starts accessing confidential projects outside their usual scope, or a contractor copies unusual directories to a USB drive, a UEBA system can flag these as anomalies. Similarly, if a user’s account credentials have been compromised, the new login patterns and command usage will diverge from baseline, generating alerts.

Once suspicious activities are identified, prioritization is key. UEBA platforms often assign a risk score to each user or event based on severity and frequency of anomalies. This lets security teams focus on the riskiest insider activities first. This could be a user who triggers multiple high-severity alerts by logging into sensitive databases they never accessed before, and the system automatically raises their risk level. This helps SOC analysts triage effectively as they can allocate resources to investigate critical cases while lower-risk anomalies can be monitored or deferred. In practice, companies that have deployed behavioral-based insider risk programs are able to preempt breaches through early warning signs in user behavior. In fact, according to a recent Ponemon Report, 65% of organizations say their insider risk program was the only strategy that enabled them to catch a breach in the making.

Faster Detection and Response

A major benefit of behavioral analytics is accelerating incident response. By catching anomalies sooner, teams can contain threats before they escalate. This is reflected in the dropping containment time reported at 81 days in 2024 vs. 86 days in 2023. Put another way, the time saved by detecting insiders early can literally save millions. DTEX research calculates that incidents resolved in under 31 days cost on average $10.6M versus $18.7M if they drag beyond 91 days. This “time is money” equation drives the ROI of analytics tools.

In fact, 63% of organizations with an insider-risk program report faster breach response as a top outcome. Whether via automated playbooks (like immediately isolating a flagged endpoint) or simply enabling analysts to act on curated alerts, behavioral insights make investigations more efficient. Importantly, faster resolution cuts downstream costs with the average per-incident containment cost climbing to $211,021 (and $154,819 for incident response), so every day saved on investigation helps reduce these big-ticket expenses. Many companies now measure success by Time to Resolution with 43% reporting shorter resolution times after adopting insider risk analytics. These metrics underscore how behavioral programs translate directly into leaner, more proactive response cycles.

AI, Budgets, and the State of Insider Risk Programs

Given the stakes, organizations are allocating more resources to insider risk. This year 81% of companies have or are planning an insider risk management program, up from 77% previously. Insider risk now commands 16.5% of the average IT security budget, a dramatic jump from 8.2% in 2023 and 46% of organizations expect their insider risk budget to grow further in 2025. This doubling of investment reflects both executive concern and recognition that specialized tools are needed to manage human-centric threats.

Artificial Intelligence (AI) is playing a growing role in these programs as well. Over half of organizations (about 54%) have begun using AI or machine learning to spot insider risks, and 51% say these tools are “essential or very important” for detection. AI-driven analytics accelerate investigations with 70% of companies citing faster investigation times as a key benefit of AI in insider risk management. The 2025 Ponemon report highlights that AI/ML reduces investigation duration, improves behavioral insights, and even lowers the specialist skill set required for analysis. Combining UEBA with AI-powered correlation means anomalous patterns across email, file systems, and network traffic can be spotted at machine speed, alerting analysts to emerging threats they might never find manually.

Benefits of Behavioral-Based Insider Risk Program

Adopting a behavioral analytics-driven insider program pays dividends. Research has shown that such programs achieve three major outcomes:

• Time Saved in Breach Response: 63% say they achieve faster response times thanks to early detection. In other words, analysts spend less time sifting through noise and more on decisive containment.
• Brand Reputation Protection: 61% report that their company’s public image is better preserved after incidents because insider-driven breaches are caught sooner. By stopping data leaks or sabotage early, organizations avoid the negative headlines and customer churn that follow a public breach.
• Reduced Financial Losses: 59% experienced lower breach-related costs when using an insider risk program. Early containment means fewer fines, legal fees, and remediation expenses, trimming the bottom-line impact of each incident.

These align with the anecdotal successes most organizations see. By detecting credential misuse immediately, for example, a behavioral system might alert a SOC before any data leave the network, effectively turning a major breach into a near miss. Over time, this proactive posture translates into measurable savings. Indeed, even as the average annual cost of insider incidents has risen, companies investing in IR management are seeing clear results with lower incremental cost and impact.

Actionable Insights and Key Metrics

CISOs and security teams should track metrics that reflect these improvements. Besides time to contain, common KPIs include number of incidents detected pre-exfiltration, mean time to detect (MTTD), and number of risky behaviors identified and remediated. According to DTEX/Ponemon data, organizations that measure success by time to resolution or time to containment see tangible improvements. Behavioral analytics tools often provide dashboards for these metrics, as well as risk heat maps by user, department, or asset.

A practical approach is to integrate UEBA with existing SIEM/SOAR workflows. Using UEBA risk scores, for example, to automate ticket creation or analyst prompts. Align these scores with business context (e.g. flag HR and finance anomalies more critically) or collect feedback if analysts validate a pattern as malicious, and have the system learn and adjust future alerts. Over time, this closed-loop use of behavioral analytics will refine both detection precision and response speed.

Ultimately, building a dedicated insider risk program that leverages behavioral science means treating human behavior as data. It means investing in analytics platforms that can discern when a deviation is benign versus a harbinger of data loss. Organizations making this investment are winning the fight against insiders, containing incidents faster, cutting costs, and safeguarding their customers and the reputation of the company.

Conclusion

In an era of increasingly more sophisticated attacks, the old defensive approach of assessing data post breach is not enough to catch “friendly fire.” Behavioral analytics and UEBA bring the needed perspective into the human element of security. By continuously monitoring user and entity actions proactively, prioritizing anomalies, and enabling rapid response, these tools transform insider risk management from reactive to proactive. The data is compelling showing companies that adopt behavioral-based insider programs report 63% faster breach response, 61% stronger reputations, and 59% less financial fallout. For CISOs, the message is clear to allocate budget and resources to robust UEBA and IR initiatives now, and be rewarded with timely detection, controlled costs, and a safer organization.

References

1. Ponemon Institute. (2025). 2025 Ponemon Cost of Insider Threats Global Report: Takeaways. Ponemon Institute.
2. Ponemon Institute. (n.d.). Ponemon Cybersecurity Report: Insider Risk Management. Ponemon Institute.
3. Ponemon Institute. (n.d.). Ponemon Cybersecurity Report Release. Ponemon Institute.
4. calHIPAA. (n.d.). Assessing the usefulness of insider risk management programs. https://www.calhipaa.com/assessing-the-usefulness-of-insider-risk-management-programs/
5. Progress Flowmon. (n.d.). How to detect insider threats: An in-depth guide. https://www.progress.com/blogs/how-to-detect-insider-threats-an-in-depth-guide
6. Waters, W. (2024, February 14). CISOs spending more on insider risk. Computer Weekly. https://www.computerweekly.com/news/366566244/CISOs-spending-more-on-insider-risk.

Author

Ann Chesbrough

Vice President of Product Marketing, BreachLock