A recent survey on the Cost of a Data Breach in 2023 reveals that organizations extensively using artificial intelligence (AI) and automation were able to save nearly $1.8 million in data breach costs and speed up the identification and containment of breaches by over 100 days, on average. However, the survey also indicates that only 28% of organizations use AI extensively, highlighting the untapped potential of this technology in cybersecurity.

The survey also found that security operations center (SOC) professionals waste approximately 33% of their time each day investigating false positives, resulting in slower threat response times. These challenges, along with a cyber skills gap and budget constraints, demonstrate the need for new technologies and approaches to strengthen organizations’ security operations.

The Importance of an AI-Driven Cybersecurity Approach

To overcome these challenges, organizations can benefit from adopting AI and automation more broadly in their security operations. The newly launched IBM Security QRadar Suite offers AI, machine learning (ML), and automation capabilities to enhance threat detection and response. QRadar EDR and QRadar SIEM, part of the suite, leverage AI and ML to quickly detect and contextualize security alerts, reducing false positives. These technologies also enable near real-time endpoint security and augment threat detection and investigation efforts.

Enhancing Endpoint Security with AI

QRadar EDR’s Cyber Assistant feature uses AI and ML to autonomously handle alerts, reducing false positives by 90%. This AI-powered system can detect and respond to previously unseen threats in near real-time, freeing up analysts’ time for higher-level analyses and threat hunting. With attack visualization storyboards, security analysts can make informed decisions and remediate both known and unknown endpoint threats using intelligent automation.

Boosting Threat Detection and Investigation with AI

QRadar SIEM employs advanced analytics, ML models, and AI to uncover hard-to-detect threats and analyze user and network behavior. By automating root-cause analysis and integrating threat intelligence, QRadar SIEM provides faster and more accurate investigations. The User Behavior Analytics app enhances your organization’s ability to react quickly to suspicious activity and predicts threats by establishing risk profiles for users. Additionally, the Network Threat Analytics app leverages advanced analytics to uncover unusual network behavior and provide real-time insights for quicker response.

By integrating AI, ML, and automation across its threat detection and response portfolio, IBM Security QRadar Suite helps organizations stay ahead of evolving threats and streamline their security operations.

Summary

Extensively using AI and automation in cybersecurity operations can lead to faster identification and containment of data breaches, resulting in significant cost savings. The adoption of AI-driven solutions, such as IBM Security QRadar Suite, can enhance threat detection, investigation, and response capabilities, freeing up analysts’ time and improving overall cybersecurity effectiveness.

FAQs

1. What is the main benefit of extensively using AI in cybersecurity operations?

Extensively using AI in cybersecurity operations can save organizations millions of dollars in data breach costs and accelerate the identification and containment of breaches.

2. What challenges do organizations face in their security operations?

Organizations face challenges such as a cyber skills gap, budget constraints, and wasted time investigating false positives, which can slow down threat response times.

3. How can AI enhance endpoint security?

AI technologies like QRadar EDR’s Cyber Assistant can autonomously handle alerts, reducing false positives and allowing security analysts to focus on more critical tasks. AI also enables near real-time threat detection and remediation.

4. How does AI boost threat detection and investigation efforts?

AI technologies, such as QRadar SIEM, use advanced analytics, ML models, and automated root-cause analysis to uncover hard-to-detect threats and analyze user and network behavior. This accelerates investigations and allows for more accurate threat detection.

5. How can organizations benefit from adopting AI-driven cybersecurity solutions?

By adopting AI-driven cybersecurity solutions, organizations can improve their overall cybersecurity effectiveness, reduce response times, free up analysts’ time, and better protect against evolving threats.