Organizations today are looking to security tools, methods, and expertise to help catch zero-day threats. Security tools should identify attacks simply by noting anomalous activity. As surface attacks on enterprises increase, identifying potential threats becomes much more difficult, and by the time strategies are developed to counter the same threats, attacks cause considerable damage. Every day, cybersecurity teams encounter a large number of alerts that are difficult to analyze, and the sheer number of connected devices in your organization makes analysis even more difficult. Artificial intelligence (AI) and machine learning (ML)-based tools can greatly help information security teams reduce the risk of breaches and improve their security posture efficiently and effectively.
According to a Capgemini Research Institute report, 61% of organizations say they cannot identify critical threats without AI, and 69% believe AI will be necessary to respond to cyberattacks. In fact, the market for AI in cybersecurity is expected to grow to $46.3 billion by 2027. AL and ML are designed and developed to continuously learn over time, deriving solutions from past data analysis and incident occurrences. Behavioral history builds profiles about users, assets, and networks, enabling AI to detect and respond to deviations from established norms. It analyzes billions of events and alerts you to current and upcoming threats. This has helped us identify malware that exploits zero-day vulnerabilities, as well as dangerous behavior that can lead to phishing attacks and malicious code downloads.
Artificial intelligence in cybersecurity is beneficial because it improves the way security professionals analyze, study and understand cybercrime. It strengthens the cybersecurity technologies companies use to fight cybercriminals and helps keep your organization and your customers safe. While there are many benefits to be gained from AI and ML, it is also very important to train AI and ML correctly with correct orientation data to help identify and thwart attacks and protect your organization’s sensitive data. is.
AI/ML systems can help in the following key areas:
- Rapid inference of anomalies and threats from learning and analysis
- Accurate fraud-related pattern detection and identification
- AI systems can be trained to identify good bots and block bad bots
- Use AI to automate discovery of all major devices and applications
- To better manage security alerts, AI-powered systems can help incident response
Advantages of artificial intelligence and machine learning
By incorporating AI and ML into your cybersecurity program, your IT team or organization can reap the following benefits:
Rapid detection of threats and alerts: With more learning, AL & ML tools can detect, analyze, and report threats faster. It does this in seconds which is not possible manually. Additionally, threats can be patched and remedied in near real-time, significantly reducing response times.
Reduced IT costs: AI and machine learning reduce the effort required to detect and respond to cyber threats, making it a cost-effective technology. According to Capgemini’s report, the average cost savings is 12%, with some organizations reducing costs by 15% or more.
Cyber analysts become more effective: Cyber analysts no longer need to manually sift through data logs. These technologies can categorize attack types, alert cyber analysts, and prepare them to respond appropriately. This puts cyber analysts in a better position to manage and counter the most complex looming threats.
Macro-level and micro-level protection of organizational infrastructure: AI and ML will learn better over time and be able to deal with complex threats. Combining past learning with current conditions improves your ability to identify suspicious activity. Improve an organization’s overall security posture by creating more effective barriers than can be achieved using manual methods.
About the author:
Gaurav Ranade is the CTO of RAH Infotech.