The Cost of a Data Breach 2023 global survey found that extensively using artificial intelligence (AI) and automation benefited organizations by saving nearly USD 1.8 million in data breach costs and accelerated data breach identification and containment by over 100 days, on average. While the survey shows almost all organizations use or want to use AI for cybersecurity operations, only 28% of them use AI extensively, meaning most organizations (72%) have not broadly or fully deployed it enough to realize its significant benefits.
According to a separate 2023 Global Security Operations Center Study, SOC professionals say they waste nearly 33% of their time each day investigating and validating false positives. Additionally, manual investigation of threats slows down their overall threat response times (80% of respondents), with 38% saying manual investigation slows them down “a lot.”
Other security challenges that organizations face include the following:
A cyber skills gap and capacity restraints from stretched teams and employee turnover.
Budget constraints for cybersecurity and perception that their organization is sufficiently protected.
Under-deployed tools and solutions that do the minimal that’s “good enough” or that face other barriers like the risk aversion to fully automating processes that could have unintended consequences.
The findings in these studies paint a tremendously strained situation for most security operations teams. Clearly, organizations today need new technologies and approaches to stay ahead of attackers and the latest threats.
The need for a more proactive cybersecurity approach using AI and automation
Fortunately, there are solutions that have shown real benefits to help overcome these challenges. However, AI and automation are often used in a limited fashion or only in certain security tools. Threats and data breaches are missed or become more severe because teams, data and tools operate in siloes. Consequently, many organizations can’t apply AI and automation more widely to better detect, investigate and respond to threats across the full incident lifecycle.
To offer a more unified analyst experience, the QRadar suite integrates core security technologies for seamless workflows and shared insights, using threat intelligence reports for pattern recognition and threat visibility. Let’s take a closer look at QRadar EDR and QRadar SIEM to show how AI, ML and automation are used.
Near real-time endpoint security to prevent and remediate more threats
QRadar EDR’s Cyber Assistant feature is an AI-powered alert management system that uses machine learning to autonomously handle alerts, thus reducing analysts’ workloads. The Cyber Assistant learns from analyst decisions, then retains the intellectual capital and learned behaviors to make recommendations and help reduce false positives. QRadar EDR’s Cyber Assistant has helped reduce the number of false positives by 90%, on average. 
This continuously-learning AI can detect and respond autonomously in near real-time to previously unseen threats and helps even the most inexperienced analyst with guided remediation and automated alert handling. In doing so, it frees up precious time for analysts to focus on higher-level analyses, threat hunting and other important security tasks.
With QRadar EDR, security analysts can leverage attack visualization storyboards to make quick and informed decisions. This AI-powered approach can remediate both known and unknown endpoint threats with easy-to-use intelligent automation that requires little-to-no human interaction. Automated alert management helps analysts focus on threats that matter, to help put security staff back in control and safeguard business continuity.
An exponential boost to your threat detection and investigation efforts
To augment your organization’s strained security expertise and resources and increase their impact, QRadar SIEM’s built-in features and add-ons use advanced machine learning models and AI to uncover those hard-to-detect threats and covert user and network behavior. QRadar’s ML models use root-cause analysis automation and integration to make connections for threat and risk insights, showing interrelationships that stretched teams might miss due to turnover, inexperience and the increased sophistication and volume of threats. It can determine root cause analysis and the orchestrate next steps based on the knowledge the models have trained on and built based on the threats your organization has faced. It gives you the information you need to reduce mean time to detect (MTTD) and mean time to respond (MTTR), with a quicker, more decisive escalation process.
Advanced analytics help detect known and unknown threats to drive consistent and faster investigations every time and empower your security analysts to make data-driven decisions. By conducting automatic data mining of threat research and intelligence, QRadar enables security analysts to conduct more thorough, consistent investigations in a fraction of the time fully manual investigations take. This spans identifying affected assets, checking indicators of compromise (IOCs) against threat intelligence feeds, correlating historical incidents and data and enriching security data. This frees up your analysts to focus more of their time and expertise on strategic threat investigations, threat hunting and correlating threat intelligence to investigations to provide a more comprehensive view of each threat. In a commissioned study conducted by Forrester Consulting, The Total Economic ImpactTM of IBM Security QRadar SIEM estimated that QRadar SIEM reduced analyst time spent investigating incidents by a value of USD 2.8 million. 
Using existing data in QRadar SIEM, the User Behavior Analytics app (UBA) leverages ML and automation to establish the risk profiles for users inside your network so you can react more quickly to suspicious activity, whether from identity theft, hacking, phishing or malware so you can better detect and predict threats to your organization. UBA’s Machine Learning Analytics add-on extends the capabilities of QRadar by adding use cases for ML analytics. With ML analytics models, your organization can gain additional insight into user behavior with predictive modeling and baselines of what is normal for a user. The ML app helps your system to learn the expected behavior of the users in your network.
As attackers become more sophisticated in their techniques, IOC and signature-based threat detection is no longer adequate on its own. Organizations must also be able to detect subtle changes in network behavior using advanced analytics that may indicate existing unknown threats while minimizing false positives. QRadar’s Network Threat Analytics app leverages network visibility to power innovative machine learning analytics that help automatically uncover threats in your environment that otherwise may go unnoticed. It learns the typical behavior on your network and then compares your real-time incoming traffic to expected behaviors through network baselines. Unusual network activity is identified and then monitored to provide the latest insights and detections. The feature also provides visualizations with analytic overlays for your network traffic, enabling your security team to save time by quickly understanding, investigating and responding to unusual behavior across the network.
Learn more about IBM Security QRadar Suite
While the challenges and complexities that cybersecurity teams face today are truly daunting and real, organizations have options that can help them stay ahead of attackers. More and more enterprises are experiencing the benefits of embracing threat detection and response solutions that incorporate proven AI, ML and automation capabilities that assist their analyst across the incident lifecycle. Relying on traditional tools and processes is no longer enough to protect against attackers that are growing more sophisticated and organized by the day.
Learn more about how the IBM Security QRadar Suite of threat detection and response products that leverage AI and automation in addition to many other capabilities for SIEM, EDR, SOAR and others by requesting a live demo.
 This reduction is based on data collected internally by IBM for nine different clients spread evenly across Europe, Middle East and Asia Pacific from July 2022 to December 2022. Actual performance and results may vary depending on specific configurations and operating conditions.
 The Total Economic ImpactTM of IBM Security QRadar SIEM is a commissioned study conducted by Forrester Consulting on behalf of IBM, April 2023. Based on projected results of a composite organization modeled from four interviewed IBM customers. Actual results will vary based on client configurations and conditions and, therefore, generally expected results cannot be provided.