How important is artificial intelligence in cybersecurity? It appears an overwhelming majority of companies consider it vital for their security posture. Data from Statista show that 83 percent of American companies say they cannot effectively respond to cyber attacks without AI.

AI’s growing role in securing IT assets is notable in threat exposure management, which entails the identification, assessment, and mitigation of potential security threats to an organization. AI plays a crucial role in this process by facilitating the automation of many of the tasks, enabling continuous monitoring, and providing a more efficient and effective way to manage threats.

Automating for greater efficiency

Many aspects of threat exposure management can be automated with AI. These are mostly repetitive tasks that do not require human decision-making. Automation is necessary to speed up their completion, reduce or even eliminate errors and missteps, and ensure a thorough and comprehensive approach to threat management.

One of the most important and tedious tasks in managing threat exposure is identifying the threat exposures and potential attack surfaces of an organization. This requires a thorough accounting of all devices connected to a network, users, apps, accounts, servers, guest devices, BYOD devices, remote work connections, SaaS (News - Alert) software supply chain, and more. It would be an exhausting task if done manually, especially for large organizations.

With the help of artificial intelligence, mapping the entire enterprise IT infrastructure and assets can be done automatically. New additions can be readily accounted for. Also, legacy devices can be discovered and handled accordingly.

Moreover, the process of spotting vulnerabilities can be automated. The use of artificial intelligence makes it possible to automatically detect misconfigurations, logic and process flaws, software that require updating, and other potential risks. Some may even be automatically addressed depending on how the system is configured.

Enabling continuous monitoring and response

AI-driven automation also has the benefit of continuous monitoring, which is a must in dealing with modern cyber threats. Threat actors nowadays are relentless and highly creative and resourceful in crafting their attacks and schemes. They can quickly come up with new attacks or tweak old ones to exploit vulnerabilities. They also know how to harness AI to automate the generation of malware and other attacks. It would be inexpedient for an organization to refuse to use AI when faced with these kinds of threats.

Aside from automating the scoping and discovery phases of continuous threat exposure management, AI can also bring automation to the prioritization, validation, and mobilization phases. It can help sort security alerts to make sure that urgent cases are promptly addressed and prevent insignificant notifications and false positives from burying critical alerts away from the urgent attention they need.

AI is also useful in automating security validation, particularly the simulation of threats and attacks. Organizations can run automatic tests to assess the integrity of security controls without having to operate a team of human white hats to continuously perform penetration testing. Several aspects of the security validation process can be automated to reduce human involvement and ensure uninterrupted readiness against emerging attacks.

Moreover, automation aids many parts of security mobilization, which are traditionally done manually. It ensures the seamless operationalization of remediation mechanisms and the generation of security information to inform and help improve subsequent threat exposure management cycles.

Reducing errors

Again, many aspects of threat exposure management are repetitive and tedious. They involve long lists of tasks and the handling of massive volumes of data. Relegating all of these to human action makes processes prone to mistakes. Human security analysts inevitably miss some items as they scour their IT infrastructure to account for all IT assets and determine attack surfaces and possible threat exposures. AI-enhanced systems can repeatedly run scoping and discovery procedures to make sure everything is properly accounted for.

Artificial intelligence may not be perfect and may not completely eliminate false positives, but it can drastically reduce security weaknesses related to detection, prioritization, and security validation. Current threat exposure management cycles yield information that helps improve the next cycles and provide insights to guide decisions with financial or business implications. It’s a never-ending learning and improvement process, where AI learns from the information fed by humans and by being constantly used to manage threats so that it can improve further in future operations.

Enabling proactive cybersecurity

In a World Economic Forum article, AI expert Santeri Kangas explains how AI serves as a key to cutting-edge cybersecurity. Kangas notes how cybersecurity has been traditionally predominantly reactive.“ Artificial intelligence is playing a major role in transforming this largely reactive nature of cybersecurity into a more proactive paradigm, especially when it comes to addressing social engineering attacks.

Threat exposure management benefits from AI-based technologies as it deals with more complex threats. It can employ natural language processing (NLP) techniques, for example, to enable machines to understand and process natural human language and more effectively deal with threats like phishing, vishing, and tailgating. NLP analyzes large amounts of texts in emails, messaging apps, and social media posts to detect threats or instances of employees unwittingly helping threat actors in operationalizing their attacks.

Artificial intelligence ushers a shift from purely rules-based to machine learning-based methods. Rules are still adopted, but they no longer strictly set the course of threat exposure management. As AI operates and learns with the inception of new information and undertaking of more threat management cycles, it becomes more adept at detecting threats and applying the appropriate responses. It establishes benchmarks of normal or safe behavior and tweaks or modifies these benchmarks based on updated threat intelligence and human-guided responses to certain complex security events.

Also, AI works with well-known cybersecurity frameworks such as MITRE ATT&CK. Aside from learning from various sources of threat intelligence and its experiences from previous threat exposure management cycles, it can also integrate frameworks that provide guidance on the detection, containment, remediation, and prevention of adversary actions based on real-world observations.

Not a silver bullet

It is important to remember that AI is a supplementary or complementary tool. It is not a silver bullet or do-it-all technology. As such, it should be employed alongside human expertise and judgment. The technology has not reached a level that makes it fully autonomous and capable of addressing emerging threats and improving its capabilities on its own. It may never reach that point, too.

Artificial intelligence is a welcome addition to effective threat exposure management. It helps achieve significant efficiency improvements and continuous monitoring and defense. It allows organizations to identify and respond to security threats more quickly. Still, it has to be viewed as a constantly evolving tool that is used not only by cybersecurity teams but also by threat actors. Hence, it is necessary to continue improving it as cyber attackers find ways to overcome effective methods at present and those that will be developed in the future.