Despite the rapid advances in technology and computing power in recent times, cyber security remains a major concern. Interconnected systems expand the attack surface and increase the challenge for security professionals. 

One way cybercriminals attack at will is by exploiting vulnerabilities in software codes to gain entry to networks. Such exploits can have devastating consequences, such as data theft, ransomware, and more. 

Several recent high-profile attacks trace their origin to threat actors targeting unpatched vulnerabilities. Examples include the 2017 Equifax breach and the 2022 LastPass breach. In 2023, attackers exploited a zero-day vulnerability to hack into the Norwegian government’s IT systems. During the year, researchers discovered a whopping 26,447+ vulnerabilities worldwide, an increase of 1500 from 2022 levels. The number of vulnerabilities has been increasing for most years. 

The obsolescence of traditional vulnerability management methods

The obvious defence against vulnerability-based attacks is removing the vulnerability. But this is easier said than done. Cybercriminals now use AI to orchestrate sophisticated attacks that overwhelm traditional security apparatus. 

The first step in vulnerability management is identifying the vulnerabilities or vulnerability assessment. Traditional methods, which involve a lot of manual work, are time-consuming. By the time the system identifies the vulnerabilities, it will be too late. Also, these methods are error-prone. Rule-based systems may not always spot the vulnerability, giving cybercriminals a free ride. About one in five endpoints continue to have vulnerabilities even after remediation.

The next step, patching, is also complex, even for well-staffed and well-funded IT teams. Patching requires balancing several constraints and software dependencies. Many of the constraints remain unknown. The task is often incomplete, as business exigencies force rushing patches to customers. The patches breaking other parts of a mission-critical system are commonplace. Memory conflicts on endpoints further degrade endpoint security. The sheer number of vulnerabilities makes prioritisation expedient.

The rise of AI-powered vulnerability assessments 

Lately, security teams have started using AI to fight fire with fire.

The latest vulnerability assessment tools leverage machine learning algorithms. These algorithms analyse code repositories, traffic, and system logs to identify patterns. Human analysis may miss subtle anomalies or patterns, but the algorithms catch anything that may indicate a threat. The detection takes place almost instantly. 

AI copilots provide greater contextual intelligence and prediction accuracy compared to first-generation tools. They also go beyond addressing current vulnerabilities. These tools apply predictive analytics to anticipate and prevent future vulnerabilities. Analysing historical data, patterns, and behaviours unearths potential weaknesses. Security experts can use such insights to deploy preventive measures. 

An added value of AI-powered vulnerability assessment tools is the ability to learn from experience. AI learns and adapts to evolving threats, improving with every experience. Machine learning algorithms identify attack patterns and add them to their knowledge base. 

AI also automates repetitive tasks, such as sifting through logs or conducting repetitive scans. Analysts, free from such mundane chores, can focus more on strategic tasks such as threat hunting and risk mitigation.

But AI-powered vulnerability assessment is not a perfect science yet. It generates several false positives, so human penetration testing is indispensable for now. But AI offers a huge upgrade from a manual setup. As artificial intelligence matures, the number of false positives will reduce.

Role of AI in patch management

Identifying and patching are two different things. Identifying vulnerabilities involves scanning and analysing the code for flaws or loose ends while patching vulnerabilities involves writing code to correct these flaws.

AI-driven patch management assists IT security teams in prioritising, validating, and applying patches. It automates the application of defences against evolving threats. The latest GenAI tools are adept at writing code but still need prompts. This might change when the AI-powered system can auto-generate prompts as well. 

The big game-changer with AI is prioritisation.

Traditional patch management methods struggle to determine which patches to apply first. Applying patches in a random or first-come-first-patched order leaves critical vulnerabilities unattended. AI-powered systems analyse the severity of the vulnerability, potential impact, and exploitability and prioritise patches based on the criticality of the vulnerability. 

AI-powered patch management also comes with intelligent capabilities for rollbacks. At times, patches cause unexpected issues or conflicts within the system, making rollback expedient. 

Of late, there have been significant breakthroughs in AI-powered vulnerability management. 

• Exploit Prediction Scoring System (EPSS), a machine-learning model, helps security IT teams manage the ever-increasing number of vulnerabilities. It adopts a risk-based patching approach, prioritising the most exploitable, business-critical threats.
• The US agency DARPA has launched Intelligent Generation of Tools for Security (INGOTS). This program leverages AI to measure and fix high-severity vulnerabilities in web browsers and mobile OS. INGOTS is still in its pilot phase.

The AI arms race

As cyber security teams use AI, threat actors also use the same AI technology. Only the motives differ. Security experts seek out vulnerabilities to generate patches and end it. Cybercriminals use AI to comb through code and discover unidentified, zero-day vulnerabilities. Thus, cyber security represents a modern-day arms race scenario. Both security teams and criminals are out to identify vulnerabilities before the other does. Many freelancers are also on the job, seeking out such vulnerabilities and making a killing by selling the information on the dark web. 

Completely automated vulnerability exploits are still a while away. The limitations of AI apply to cybercriminals as well. Thus, cybercriminals cannot use AI to write vulnerability exploits yet, without human guidance. Most attackers buy exploit kits rather than write customer code.

The zero-trust approach to future-proof against vulnerability exploits 

All networks are vulnerable to compromise. A determined attacker, given enough time, can breach any network.  

The most effective way to secure the network in such an environment is to assume that a compromise has occurred. Forward-thinking cyber security teams assume that attackers are already inside the network. They microsegment the network so that attackers cannot extend their reach beyond the segment they have accessed. 

The Zero Trust approach enables such microsegmentation. 

Imagine an attacker exploiting a vulnerability to enter the network. If zero trust is in place, he must provide authentication to proceed to the next network segment. Thus, the attacker is denied a large foothold.

Zero Trust’s success depends on deploying the right tools, such as Cloudflare. Zero trust works through secure web gateways, DNS filtering, and data loss prevention. Cloudflare’s integrated platform consolidates all these technologies through an integrated platform. The unified dashboard makes administration and control super easy. Cloudflare’s distributed network allows admins to enforce granular controls without latency.