Artificial Intelligence (AI) is changing the way various industries operate, and cybersecurity is no exception. Over the years, cyber threats have been complex and frequent, and the need for advanced, adaptive security measures is greater than ever. AI and Machine Learning (ML) offer powerful tools to enhance cybersecurity defenses, but they also bring new challenges and risks.
This article examines how AI will impact cybersecurity, highlighting its implications for Security Information and Event Management (SIEM) systems.
Main Challenges Cybersecurity Faces Today
Imagine a situation where an organization is facing a complex, multi-vector cyber attack, and AI is incorporated into the SIEM architecture components. This will help traditional security measures fight the threat effectively because AI-driven systems can analyze vast amounts of data in real time, identify the threats, and initiate defensive measures almost instantaneously.
Geographically Distant IT Systems
Modern organizations often operate across multiple regions, making manual tracking of security incidents complex and inefficient. The distance makes it complicated to monitor and coordinate cybersecurity efforts, as infrastructure and network configurations can hinder effective incident management.
Manual Threat Hunting
Traditional threat-hunting methods are time-consuming and costly, which often results in delayed responses and overlooked attacks.
Reactive Nature of Cybersecurity
Many cybersecurity strategies respond to incidents more often after they occur. Predicting and tackling cyber threats are a huge challenge for security experts.
Hacker Evasion Techniques
Cybercriminals continuously develop new techniques to evade detection. They hide their identities and locations using tools like Virtual Private Networks (VPNs), proxy servers, and Tor browsers.
AI and Cybersecurity
Cybersecurity is one of AI’s most popular use cases. According to a report by Norton, the global cost of a typical data breach recovery is $3.86 million, and organizations take an average of 196 days to recover. So, Investing in AI can reduce these costs and recovery times by enhancing threat detection and response capabilities.
AI, Machine learning, and threat intelligence can identify patterns and predict future threats. In addition, AI and ML can analyze vast amounts of data at the speed of light, ensuring organizations permit threats.
Threat Hunting
Traditional security techniques rely heavily on signatures or indicators of compromise to identify threats. This trick is often not effective for known threats and can only prove threat detection for around 90% of threats. AI can improve threat hunting by 95% by integrating behavioral analysis, allowing for the detection of previously unknown threats.
To make this work effectively, users should combine both traditional and AI solution tools like Stellar Cyber. This can result in a 100% detection rate and decrease the chance of falsehood.
Vulnerability Management
The number of reported vulnerabilities is increasing rapidly, with over 20,362 new vulnerabilities reported in 2019 alone, which was up by 17.8% from 2018. Traditional vulnerability management methods often wait for vulnerabilities to be exploited before taking action.
With AI and machine learning techniques like User and Event Behavioural Analytics (UEBA), organizations can help address this issue by identifying anomalies that may indicate a zero-day attack. This proactive approach helps protect organizations from threats even before vulnerabilities are officially reported and patched.
Data Centers
AI can optimize and monitor critical data center processes, such as power consumption, cooling, and bandwidth usage. Its continuous monitoring capabilities provide insights into how to improve the effectiveness and security of data center operations.
In addition, AI can alert users when to fix or maintain hardware equipment. These alerts enable users to take charge before the equipment goes terrible. For instance, Google reported a 40 percent reduction in cooling costs and a 15 percent reduction in power consumption after implementing AI in their data centers. These improvements not only enhance operational efficiency but also contribute to a more secure and resilient infrastructure.
Network Security
Traditional network security tasks are time-intensive, such as creating security policies and understanding the network topology. Both are prone to errors. AI can simplify these processes by learning network traffic patterns and recommending functional groupings of workloads and security policies.
‘ Policies‘Security policies define which network connections are legitimate and which ones need further inspection for potential malicious activity. These policies are essential for enforcing a zero-trust model. However, creating and maintaining these policies is challenging due to the large number of networks.
‘ Topography’Many organizations lack consistent naming conventions for applications and workloads. This forces security teams to spend significant time identifying which workloads belong to specific applications.
Drawbacks and Limitations of Using AI for Cybersecurity
Although AI has good benefits, there are still limitations to it becoming a mainstream security tool:
Resource Intensive
Organizations will need to invest in computing power, memory, and data and maintain AI systems. Not all organizations have the resources to support these requirements, which can limit the adoption of AI-based cybersecurity solutions.
Data Set Requirements
AI models need large, diverse datasets to learn effectively. Security teams have to gather extensive data on malicious codes, malware, and anomalies. Not all organizations can acquire and secure these datasets, and moreover, it can be time-consuming and costly.
Adversarial Use of AI
Cybercriminals can also use AI to enhance their attacks. They try to study existing AI tools and develop more sophisticated malware and tactics to bypass traditional systems or even AI-driven defenses.
Neural Fuzzing
Fuzzing is a process that involves testing software with large amounts of random input data to identify vulnerabilities. Neural fuzzing uses AI to accelerate this process, potentially uncovering weaknesses faster. However, attackers can also use this technique to identify and exploit vulnerabilities in target systems. Stellar Cyber is a solution tool that can be used to secure traditional system software code, making it hard to exploit.
Fighting Against AI Cyberattacks with AI-Powered Cybersecurity
Hardening the System
AI-powered code analysis tools can scan software code to identify errors, insecure practices, and potential vulnerabilities. By detecting these issues early in the development process, organizations can address security risks before they are exploited.
Additionally, AI-powered penetration testing can simulate cyberattacks, uncover vulnerabilities, and strengthen defenses.
Improving Threat Detection
AI-driven anomaly detection, behavior-based analytics, and user behavior analytics play important roles in identifying and mitigating cyber threats. These tools compare real-time data against historical baselines to detect unusual activity. For instance, deep packet inspection can analyze network traffic at a granular level, helping organizations identify and respond to intrusions more effectively.
Faster Incident Response
AI can improve incident response capabilities by analyzing the severity, impact, and context of security incidents. Automating the analysis of security events will enable security teams to prioritize their response efforts and focus on the most critical threats first. AI can also help an organization investigate a threat by analyzing telemetry data and providing details on the cause.
Conclusion
AI is set to change cybersecurity by offering powerful tools to organizations and users to enhance threat detection, response, and security management. However, the rise of AI in cybersecurity is a double-edged sword. On one hand, it can identify patterns and anomalies far more efficiently than traditional methods. On the other hand, cybercriminals can exploit the technology to develop more sophisticated attacks.
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