As human dependence on technology increases, securing interconnected digital systems has become crucial. Despite the growing need for cybersecurity professionals, the skills shortage in this field remains a major problem for decision-makers around the world. Can AI fill this gap?
Today, computer systems process massive volumes of digital data, including personally identifiable informationmedical records and financial information. As a result, malicious actors have increased their efforts to exploit these systems and gain access to this sensitive information.
To secure their IT systems, organizations around the world are increasing their security spending. A significant portion of this spending goes toward hiring experts to manage systems and oversee the implementation of security policies and access controls.
But there are currently not enough qualified experts to fill existing job openings for cybersecurity professionals. While digital transformation is accelerating, the number of cybersecurity professionals is not increasing at the same rate. According to research According to Cybersecurity Ventures, there were 3.5 million unfilled cybersecurity positions in 2023, including 750,000 in the United States – a disparity that is expected to persist until at least 2025.
The rise of AI, in particular Generative AI tools like OpenAI ChatGPToffers organizations new ways to close the cybersecurity skills gap by using AI and machine learning (ML) to strengthen cybersecurity defenses. However, the essential question remains: can AI replace human experts entirely?
The limits of AI in cybersecurity
While AI and machine learning can simplify many cybersecurity processes, organizations cannot eliminate the human element from their cyber defense strategies. Despite their capabilities, these technologies have limitations that often require human understanding and intervention, including a lack of context understanding and vulnerability to inaccurate results, adversarial attacks, and bias.
Because of these limitations, organizations should view AI as an enhancement to, not a replacement for, human cybersecurity expertise. AI can augment human capabilities, especially when dealing with large volumes of threat data, but it cannot fully replicate the contextual understanding and critical thinking that human experts bring to cybersecurity.
Lack of contextual understanding
AI tools can analyze vast volumes of data, but lack the human capacity to understand the psychological elements of cyber defense, such as hackers’ 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 crucial when dealing with complex zero-day threats that require a deep understanding of context.
Inaccurate results
AI tools can issue incorrect alerts – both false positives and false negatives. False positives can waste resources, while false negatives can leave organizations vulnerable to threats. Therefore, humans must review AI-generated alerts to ensure they don’t miss critical threats, while avoiding costly and unnecessary investigations.
Contradictory attacks
As enterprise AI deployments become more widespread, adversarial machine learning — attacks on AI and ML models — are expected to increase. For example, malicious actors could poison a model used to power an AI malware scanner, causing it to misidentify malicious files or code as benign. Human intervention is essential to identify and respond to these manipulations and ensure the integrity of AI-powered systems.
AI bias
AI systems can be biased if they are trained on biased or unrepresentative data, leading to inaccurate results or biased decisions that can significantly impact an organization’s cybersecurity posture. Human oversight is necessary to mitigate these biases and ensure that cybersecurity defenses operate as intended.
How AI Can Alleviate the Cybersecurity Skills Shortage
As cyber threats become more sophisticated, security tools must evolve accordingly to stay ahead. While human security professionals are essential to any cybersecurity protocol, AI and ML can contribute to security modernization efforts in several ways.
Automated threat intelligence detection
AI can automate threat detection and analysis by analyzing huge volumes of data in real time. AI-powered threat detection tools can quickly identify and respond to cyber threats, including emerging threats and day zero attacks, before they penetrate an organization’s network.
AI tools can also combat insider threats, a major concern for modern organizations. After learning the normal behavior patterns of users within the organization’s IT environment, AI tools can flag suspicious activity when a particular user deviates from these patterns.
When it comes to spam protection, AI-powered tools offer significant advantages over traditional methods. natural language processing With their capabilities, AI-powered tools can process a wider range of data types, including unstructured data such as social media posts, text and audio files, in addition to emails and business documents. This capability improves the overall effectiveness of threat detection.
Vulnerability Management
Vulnerability Management is essential to any cybersecurity defense strategy. Most cyberattacks exploit vulnerabilities in software applications, computer networks, and operating systems to gain entry into the targeted IT environment. Rigorous vulnerability management can mitigate many types of cyber threats.
A typical vulnerability management system includes the following elements:
- Identification.
- Categorization.
- Sanitation.
- Continuous monitoring.
But traditional vulnerability management tools have many disadvantages, such as reliance on manual efforts, low speed, and lack of contextual analysis. AI capabilities can mitigate these disadvantages in the following ways:
- Automation of repetitive tasks such as analysis, evaluation and correction.
- Prioritization of vulnerabilities based on their severity and impact on the business.
- Provide continuous, real-time monitoring to detect emerging threats and zero-day attacks, significantly reducing an organization’s attack surface.
- More accurately identify future vulnerabilities through continuous learning.
- Automatic recommendation of fixes and mitigations.
IT forensics and incident response
AI-based tools can improve various aspects of digital forensics and incident response.
For example, after a cybersecurity incident, machine learning algorithms can analyze vast volumes of unstructured data, such as server and network logs, to identify the source and scope of the attack. These tools excel at responding quickly to contain the incident and prevent further damage to other IT assets.
AI tools can also significantly improve the remediation process after cybersecurity incidents. A large part of the incident lifecycle involves restoring systems to normal operation after the breach is discovered. IT administrators use a multitude of tools and scripts to meet the following needs:
- Analysis of malware artifacts.
- Removing malicious files.
- Disabling compromised user accounts.
- Disconnecting compromised endpoint devices from the network.
- Collecting forensic evidence from affected systems, such as log files and memory dumps.
- Applying patches to fix security vulnerabilities.
Generative AI tools such as ChatGPT and GitHub co-pilot can help create scripts in any programming language to automate these repetitive tasks, streamlining the incident response process.
Security orchestration
AI-powered security tools can automate many tasks related to security configuration and management, including:
- Configuring firewall rules by analyzing normal user behavior and interactions on the internal network and then generating firewall rules based on that behavior.
- Update systems and applications and roll back changes if problems appear.
- Analyze historical network data to identify intrusion detection And prevention system configurations.
- Analyzes cloud configurations to identify and correct possible configuration errors that malicious actors could exploit.
Nihad A. Hassan is an independent cybersecurity consultant, digital forensics and open source cyber intelligence expert, blogger and book author. Hassan has been actively researching various areas of information security for over 15 years and has developed numerous training courses and technical guides on cybersecurity.
