AI driven Vulnerability Management
Every organization uses multiple projects and programs to manage its business. Typically, a cybersecurity project focuses on a
specific problem and a set of short-term deliverables. The cybersecurity program, on the other hand, consists of multiple related projects addressing cybersecurity challenges that can have wide-ranging effects across the enterprise. While the project is designed to address a discrete challenge, the program is designed to push the organization forward on high-level strategies, goals, and directives.
In terms of vulnerability management, we often see the application of individual vulnerability scanners and projects in various parts of an organization. But the truly effective vulnerability management program operates at a higher level.
The conventional approach to vulnerability and threat management is rapidly changing to a data-driven strategy in which remediation efforts are targeted to individual vulnerabilities based on their exploitability, exploit pulse and environment.
This approach will revolutionize vulnerability management – especially in the remediation of most common vulnerabilities that are frequently exploited – but its success depends on the availability of reliable data, which can have many biases and uncertainties.
Weaponization analysis can be used for early warning, diagnosis, remediation prioritization, and prescriptive information on what to fix and how to fix vulnerabilities that matter.
Artificial Intelligence is an umbrella term that encompasses several areas of advanced computer science, everything from speech recognition to natural language processing, to robotics, to symbolic and deep learning. AI technologists are constantly striving to automate seemingly “intelligent” behaviour, or put differently, programming computers to do historically human tasks.
Vulnerability Management Tools (vulnerability scanners, deep learning, and AI)
As our understanding of security risk has matured, so have vulnerability management tools, which now support a continuous enterprise-wide lifecycle of vulnerability discovery, remediation, and reporting.
“A full-featured vulnerability management product or suite of products must be able to support, at minimum, a repeatable lifecycle of asset discovery and enumeration, vulnerability detection, risk assessment, configuration compliance assessment, change management and remediation, verification, and auditing and reporting.”
Vulnerability scanning tools are the backbone of every vulnerability management program. They don’t just perform vulnerability and error detection; they also help with risk assessment based on the severity of the threat and the value of the vulnerable system to the organization. After remediation, re-scans will tell you if corrective actions have been successful (that is, a patch has been successfully applied or the configuration error corrected).
It’s also worth noting that while machine learning and AI are emerging technologies that can be applied to nearly every sector, there are many fields that are either reaping the benefits of AI right now or that soon will be and cybersecurity is one of them. Because of the unique challenges that cybersecurity presents (vast attack surface, hundreds of attack vectors, thousands of devices, masses of data), artificial intelligence and autonomous systems can often automate threat detection and respond more efficiently than traditional software-driven approaches are equipped to do.
Usage of AI-ML In Vulnerability Management
Within this year alone, there have been over 2,000 unique vulnerabilities reported. Managing all of these with human resources or traditional technology is extremely difficult. AI, however, can tackle this with a lot more ease.
Systems based on AI-ML do not wait for a vulnerability to be exploited by online threats. Instead, these AI-based systems proactively look for potential vulnerabilities in organizational information systems, and they do so by effectively combining multiple factors, such as hackers’ discussions on the dark web, reputation of the hacker, patterns used, etc. These systems can analyze these factors and use the information to determine when and how the threat might make its way to vulnerable targets.
Is AI an answer to all my cybersecurity problems?
While the thought of entirely letting AI takeover is very tempting, we must remember that AI consists of a lot of things and, therefore, is very versatile. While AI is doing wonders for cybersecurity, it is also making its way into the employ of hackers for malicious purposes. In the wrong hands, it can do exponential damage and become an even stronger threat to cybersecurity.
As technology evolves, the adversaries are also enhancing their attack methods, tools, and techniques to exploit individuals and organizations. There’s no doubt that Artificial Intelligence is incredibly useful, but it is somewhat of a double-edged sword. AI-ML can be used to detect and prevent the attacks before it takes place. As AI sees more advancement, we will be witnesses to how far we can take this technology to be both a boon and a bane to cybersecurity and the society in general.