As humans become more reliant on technology, securing our interconnected digital systems is crucial. Despite the growing demand for cybersecurity professionals, the skills shortage in this field remains a major issue for decision makers around the world. Can AI fill this gap?
Today, IT systems process vast amounts of digital data, including personal information, medical records, financial details, etc. As a result, threat actors are stepping up their efforts to exploit these systems to access sensitive information.
To ensure the security of their IT systems, organizations around the world are increasing their security spending, with a large portion of this spending going to hiring professionals to manage the systems and oversee the implementation of security policies and access controls.
However, there is currently a shortage of qualified professionals to fill existing cybersecurity professional openings. While digital transformation is accelerating, the number of cybersecurity professionals is not growing at the same rate. According to a Cybersecurity Venture study, 3.5 million cybersecurity jobs will go unfilled in 2023, 750,000 of which will be in the United States, and this gap is predicted to continue until at least 2025.
The rise of AI, particularly generative AI tools like OpenAI's ChatGPT, gives organizations new ways to use AI and machine learning (ML) to bolster their cybersecurity defenses and overcome the cybersecurity skills gap. But a key question remains: can AI fully replace human experts?
The limits of AI in cybersecurity
While AI and ML can streamline many cybersecurity processes, organizations cannot eliminate the human element from their cyber defense strategy. Although these technologies have impressive capabilities, they are limited by limitations that often require human insight and intervention, including poor contextual understanding, susceptibility to inaccurate results, adversarial attacks, and bias.
These limitations mean that organizations should view AI as an augmentation of human cybersecurity expertise, not a replacement. While AI can augment human capabilities, especially when dealing with large amounts of threat data, it cannot fully replicate the situational understanding and critical thinking that human experts bring to cybersecurity.
Lack of context
While AI tools can analyze vast amounts of data, they lack the human ability to understand the psychological aspects of cyber defense, such as hacker motivations and tactics. This understanding is essential to predicting and responding to advanced persistent threats and other sophisticated attacks such as ransomware. Human intervention is essential to address complex zero-day threats that require deep contextual understanding.
Inaccurate Results
AI tools can sometimes issue false alerts, including both false positives and false negatives. False positives can lead to wasted resources, while false negatives can make your organization vulnerable to threats. Therefore, AI-generated alerts need to be reviewed by humans to ensure that critical threats aren't missed and to avoid costly and unnecessary investigations.
Hostile attacks
As AI adoption in the enterprise becomes more widespread, we expect to see an increase in adversarial machine learning (attacks against AI and ML models). For example, threat actors may trick models used to drive AI malware scanners into falsely identifying malicious files or code as benign. Human intervention is essential to identify and respond to such manipulation and ensure the integrity of AI-powered systems.
AI Bias
AI systems can become biased when trained with biased or unrepresentative data, leading to inaccurate results and biased decisions that can have serious implications on an organization's cybersecurity posture. Human oversight is required to mitigate such bias and ensure cybersecurity defenses are working as intended.
How AI can help ease the cybersecurity skills shortage
As cyber threats grow more sophisticated, security tools must evolve accordingly to stay one step ahead. While security experts are essential to any cybersecurity protocol, AI and ML can aid security modernization efforts in a variety of ways.
Automated Threat Intelligence Detection
By scanning vast amounts of data in real time, AI can automate threat detection and analysis. AI-powered threat detection tools can quickly identify and respond to cyber threats, including emerging threats and zero-day attacks, before they can enter an organization's network.
AI tools can also combat insider threats, a major concern for modern organizations: After learning normal patterns of user behavior within an organization's IT environment, AI tools can flag suspicious activity when a particular user deviates from these patterns.
When it comes to fighting spam, AI-powered tools offer a significant advantage over traditional methods. Thanks to their natural language processing capabilities, AI-powered tools can process a wider range of data types, including emails and business documents, as well as unstructured data such as social media posts, text files, and audio files. This capability improves the overall effectiveness of threat detection.
Vulnerability Management
Vulnerability management is a key part of any cybersecurity defense strategy. Most cyber attacks exploit vulnerabilities in software applications, computer networks, and operating systems to gain entry into a target's IT environment. Robust vulnerability management can help mitigate many types of cyber threats.
Common vulnerability management systems include:
- identification.
- Classification.
- repair.
- Continuous monitoring.
However, traditional vulnerability management tools have many drawbacks, including reliance on manual effort, slow speed, lack of contextual analysis, etc. AI capabilities can help mitigate these drawbacks by:
- Automate repetitive tasks like scanning, assessment and remediation.
- Prioritize vulnerabilities based on severity and business impact.
- It provides continuous real-time monitoring to detect emerging threats and zero-day attacks, significantly reducing an organization's attack surface.
- Continuous learning helps to better identify future vulnerabilities.
- Automatically recommend patches and mitigations.
Digital Forensics and Incident Response
AI-powered tools can enhance many aspects of digital forensics and incident response.
For example, after a cybersecurity incident occurs, machine learning algorithms can analyze vast amounts of unstructured data, such as server and network logs, to identify the source and scope of the attack. These tools are excellent for responding quickly to contain the incident and prevent further damage to other IT assets.
AI tools can also significantly improve the remediation process after a cybersecurity incident. A large part of the incident lifecycle is restoring systems to normal operation after discovering a breach. IT administrators use a variety of tools and scripts to meet these needs:
- Analyze malware artifacts.
- Deleting malicious files.
- Disable the compromised user account.
- Disconnect the compromised endpoint device from the network.
- Collect forensic evidence from affected systems, such as log files and memory dumps.
- Apply patches to fix security vulnerabilities.
Generative AI tools like ChatGPT and GitHub Copilot can help you create scripts in any programming language to automate these repetitive tasks and streamline your incident response process.
Security Orchestration
AI-powered security tools can automate many tasks related to security configuration and management, including:
- It configures firewall rules by analyzing normal user behavior and interactions across your internal network, and then generates firewall rules based on that behavior.
- Update your system and applications, and revert the changes if any issues arise.
- Analyze historical network data to identify optimal intrusion detection and prevention system configurations.
- Scan your cloud configurations to identify and remediate misconfigurations that malicious actors could exploit.
Nihad A. Hassan is an independent cybersecurity consultant, digital forensics and cyber open source intelligence expert, blogger, and book author. Hassan has been actively researching various areas of information security for over 15 years and has developed numerous cybersecurity educational courses and technical guides.
