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.The newly launched IBM Security QRadar Suite offers AI, machine learning (ML) and automation capabilities across its integrated threat detection and response portfolio, which includes EDR, log management and observability, SIEM and SOAR. As one of the most established threat management solutions available, QRadar’s mature AI/ML technology delivers accuracy, effectiveness and transparency to help eliminate bias and blind spots. QRadar EDR and QRadar SIEM use these advanced capabilities to help analysts quickly detect new threats with greater accuracy and contextualize and triage security alerts more effectively.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. [1]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.