AI prevents data breaches with lower expenses

Data breaches Prevented by AI

According to the Cost of a Data Breach 2023 worldwide survey, employing artificial intelligence (AI) and automation greatly helped enterprises by saving them up to USD 1.8 million in expenses associated with data breaches and accelerating data breach discovery and containment by, on average, over 100 days. While the poll indicates that virtually all firms utilize or wish to employ AI for cybersecurity operations, just 28% of them do so substantially, implying that the majority of organizations (72%) have not deployed it widely or thoroughly enough to enjoy its substantial benefits.

Separate research from the 2023 Global Security Operations Center Study claims that SOC workers squander about 33% of their daily time looking for and confirming false positives. Additionally, 80% of respondents said that manual analysis of threats slows down their total threat response times, with 38% claiming that it slows them down “a lot.”

The following are some other security difficulties that businesses encounter:

• a lack of cyber skills, capacity limitations brought on by overworked teams, and staff churn.
• Budgetary restrictions and the belief that their organization is adequately safeguarded.
• Tools and solutions that haven’t been fully implemented or that just accomplish the bare minimum that’s “good enough” or that encounter additional obstacles like a reluctance to fully automate operations that can have unexpected repercussions.

The results of these research portray a highly stressful condition for the majority of security operations personnel. It is obvious that modern enterprises require new technology and methods to keep one step ahead of attackers and the most recent dangers.

The requirement for a more proactive cybersecurity strategy employing automation and AI

Luckily, there are several approaches that have really helped overcome these difficulties. Automation and artificial intelligence are, however, frequently utilized sparingly or solely in certain security tools. Teams, data, and tools work in silos, which causes threats and data breaches to be undetected or to worsen. As a result, many businesses are unable to use AI and automation more broadly in order to more effectively detect, research, and react to risks over the whole incident lifecycle.

The recently released IBM Security QRadar Suite, which comprises EDR, log management and observability, SIEM, and SOAR, delivers AI, machine learning (ML), and automation capabilities throughout its integrated threat detection and response portfolio. The sophisticated AI/ML technology used by QRadar, one of the most well-known threat management products on the market, combines accuracy, efficacy, and transparency to help remove bias and blind spots. These cutting-edge features are used by QRadar EDR and QRadar SIEM to assist analysts in contextualizing and triaging security alerts more successfully as well as identifying new threats more rapidly and accurately.

The QRadar package uses threat intelligence reports for pattern identification and threat visibility, integrating key security technologies for smooth workflows and shared insights. Let’s examine QRadar EDR and QRadar SIEM in more detail to demonstrate how AI, ML, and automation are utilized.

Endpoint protection in near real-time to stop and remove more threats

Cyber Assistant, a component of QRadar EDR, is an AI-powered alert management solution that employs machine learning to automatically handle alerts and lessen the strain of analysts. The Cyber Assistant picks up knowledge from analyst judgments, then uses it together with newly learnt behaviors to offer suggestions and lessen false positives. The average number of false positives has been reduced by 90% thanks to QRadar EDR’s Cyber Assistant. [1]

Even the most unskilled analyst may benefit from guided remediation and automatic warning handling thanks to this constantly improving AI, which can identify and respond autonomously in close to real-time to previously unidentified risks. By doing this, it gives analysts valuable time back so they may concentrate on more in-depth studies, threat hunting, and other crucial security responsibilities.

Security analysts may use attack visualization storyboards with QRadar EDR to quickly and intelligently make judgments. This AI-powered strategy can eliminate both known and unidentified endpoint threats using intuitive automation that is simple to deploy and almost human interaction-free. Automated alert management aids analysts in concentrating on threats that matter, restoring control to security employees and ensuring business continuity.

Your attempts to identify threats and conduct investigations will be greatly accelerated

The built-in capabilities and add-ons of QRadar SIEM leverage cutting-edge machine learning models and AI to find those difficult-to-detect threats and covert user and network activity, enhancing your organization’s already stretched security skills and resources and increasing their effectiveness. Due to turnover, inexperience, and the rising sophistication and number of threats, stretched teams may overlook interrelationships that QRadar’s ML models leverage root-cause analysis automation and integration to highlight. Based on the knowledge the models have been trained on and created based on the dangers your firm has experienced, it may establish root cause analysis and the organize future measures. With a speedier, more effective escalation procedure, it provides you with the knowledge you need to decrease mean time to detect (MTTD) and mean time to react (MTTR).

Advanced analytics aid in the detection of known and unidentified risks to generate repeatable, quick investigations and equip your security analysts to make fact-based judgments. Security analysts may perform more complete, consistent investigations using QRadar’s automated data mining of threat research and information in a tenth of the time it takes to do entirely manual investigations. Indicators of compromise (IOCs) are checked against threat intelligence feeds, past events and data are correlated, and security data is enhanced. Your analysts will now have more time and resources to devote to strategic threat investigations, threat hunting, and the correlation of threat intelligence to investigations in order to present a more complete picture of each threat. The Total Economic ImpactTM of IBM Security QRadar SIEM estimated that QRadar SIEM decreased analyst time spent investigating events by a value of USD 2.8 million in a research that was commissioned by Forrester Consulting. [2]

The User Behavior Analytics app (UBA) uses the already-existing data in QRadar SIEM and makes use of machine learning (ML) and automation to create risk profiles for users inside your network. By doing this, you can respond more quickly to suspicious activity, such as identity theft, hacking, phishing, or malware, and you can more accurately identify and anticipate threats to your organization. By including ML analytics use cases, UBA’s Machine Learning Analytics add-on expands the capabilities of QRadar. Your business can learn more about user behavior with ML analytics models by using baselines for typical user behavior and predictive modeling. Your system can learn the typical user behavior in your network with the aid of the ML app.

IOC and signature-based threat detection are no longer sufficient on their own as attackers develop more complex approaches. Additionally, organizations need to be able to use advanced analytics to spot tiny changes in network activity that could point to undetected risks already present while limiting false positives. The network visibility provided by QRadar’s Network Threat Analytics software powers cutting-edge machine learning analytics that automatically identify risks in your environment that could otherwise go undetected. It becomes familiar with your network’s regular behavior and uses network baselines to compare your current incoming traffic to those expectations. The latest insights and detections are provided by first identifying unusual network behavior and afterwards monitoring it. Additionally, the functionality offers network traffic visualizations with analytical overlays, which helps your security team save time by swiftly identifying, analyzing, and reacting to unexpected network behavior.

Become familiar with IBM Security QRadar Suite

While enterprises have alternatives that can help them keep one step ahead of attackers, the difficulties and complexity that cybersecurity professionals face today are truly challenging and real. The advantages of adopting threat detection and response systems that include tested ML, AI, and automation capabilities that support its analysts throughout the incident lifecycle are being realized by an increasing number of businesses. Traditional methods and technologies are no longer sufficient to fend off attackers, who are getting more organized and clever every day.

Request a live demo to learn more about the IBM Security QRadar Suite of threat detection and response tools, which also includes many more features for SIEM, EDR, SOAR, and other systems.