Cybersecurity is becoming increasingly complex due to the surge in new data sources and changes in how data is utilized.

However, artificial intelligence (AI) can provide more intelligent security strategies by comprehensively considering large-scale contexts.

The fact that companies are processing vast amounts of data is no longer a new story.

However, the number of data sources and the resulting attack surface continue to increase. The proliferation of agentic AI, autonomous bots, the Industrial Internet of Things (IIoT), generative AI, and large-scale language models (LLM) is increasing opportunities for threat actors to infiltrate systems, while simultaneously making the network architectures that companies must monitor even more complex.

Just as threat actors can utilize AI to carry out cyber attacks, companies can also use the same technology to strengthen their security systems.

In fact, the field of cybersecurity is evolving rapidly as it leverages the potential of AI on a large scale.

In this article, we introduce five strategies to strengthen cybersecurity using AI.

The main points are as follows.
o Implementation of security strategies across the distributed environment
o Overcoming the limitations of static signature-based security
o Context-based gatekeeping automation
o Prioritization and optimization of exposure and vulnerability management
o Analysis of the psychology and behavioral patterns of threat actors

■ Implementation of security strategies across the distributed environment

Cybersecurity must consider how and where data moves in both on-premises and cloud environments.

In addition, all such data must be protected regardless of whether it is in storage or in transmission.

A zero-trust framework that validates all access requests helps meet these security requirements.

Applying Role-Based Access Control (RBAC) here enables intelligent and granular access management for sensitive data.

AI can effectively implement these security strategies in large-scale environments.
<bIn particular, in Industrial Internet of Things (IIoT) environments, attacks can occur in the form of unauthorized command execution instead of traditional malware infection methods, and AI can effectively respond to these new threats.

AI plays a crucial role in implementing cybersecurity in today's highly distributed data environment.

In the process of analyzing various data such as telemetry, network traffic, and equipment status, AI learns what constitutes a 'normal state' and can detect abnormal signs deviating from that standard within an appropriate context.

For example, AI agents can be trained to protect specific attack surfaces, and proprietary machine learning (ML) models can also be trained to detect threats specialized for an enterprise's unique workflows.

In addition, damage caused by cyber breaches can be minimized by automating initial response measures, such as isolating the breached environment or restricting network access, as part of an AI-based defense system.

■ Overcoming the limitations of static signature-based security

One of the long-proven cybersecurity strategies is to database information on past threat actors and compare and detect new attacks based on existing 'signatures' such as IP addresses.

While such signature-based detection can be an important element of a security system, it is not enough on its own.

Threat actors can change signatures very quickly, and existing methods cannot effectively respond to zero-day attacks originating from entirely new sources.

The drift recognition AI model tracks changes in the network environment, including cloud autoscaling and remote work, and monitorsThis allows for the continuous updating of internal records regarding potential new attack surfaces.

Machine learning (ML) models group events based on behavioral similarities instead of static, unchanging signatures that can quickly become obsolete.

Equally important is that this mechanism can withstand the explosion of data generated from distributed nodes, ranging from hundreds of thousands to as many as millions.

■ Context-based Gatekeeping Automation

Because some of the most complex infrastructures integrate Information Technology (IT) and Operational Technology (OT), AI models analyze how devices communicate and respond to commands.

If the command sequence deviates from the existing normal pattern, the AI marks the activity as suspicious behavior.

In addition, threat updates continuously provided from Security Information and Event Management (SIEM), Endpoint Detection and Response (EDR), Network Detection and Response (NDR), and Software as a Service (SaaS) logs can help in gaining a comprehensive understanding of the situation.

However, responding to every single warning may not be realistically appropriate, and this is precisely why cybersecurity teams need information about the context of threats.

Context enhancement models combine additional relevant information with data to improve the understanding of events.

In this environment, AI models can assign probability-based risk scores to threats instead of a simple binary approach.

In addition, cybersecurity strategies must also take into account that the assigned risk level may increase over time.

■ Prioritization and Optimization of Exposure and Vulnerability Managementstrong>

Enterprises possess hundreds of thousands of Common Vulnerabilities and Exposures (CVEs), and there are limits to their ability to respond to all evolving threats in an environment where asset lists are constantly changing.

AI-based predictive analytics helps cybersecurity teams identify which vulnerabilities are actually likely to be exploited.

In addition, AI can identify the most likely attack paths and target IDs.

All these attack surfaces must be considered comprehensively within an interconnected context.

For example, a CVE existing in an isolated system at an external site may have a lower overall level of vulnerability than a highly privileged user account using a commercial browser for daily tasks.

■ Understanding the Psychology of Threat Actors

Cybersecurity teams can also utilize psychological warfare (psyops) techniques to understand and influence the psychology of the 'enemy'.

AI can strengthen cyberpsychology-based security systems by exploiting attackers' cognitive weaknesses.

For example, it is possible to leverage the innate biases of an attacker to induce the next response step of a cyber defense system.

While such systems may not be able to permanently block threat actors, they can delay or disrupt attacks enough to buy time for stronger defensive measures to take effect.

Currently, these advanced techniques are effectively applied mainly to human-led cyber attacks, but are used to a limited extent in AI-based bot attacks.

However, this field is also rapidly evolving to counter next-generation AI threat actors. />
■ Conclusion

As cyber attackers utilize increasingly sophisticated AI-based bots, AI itself is also establishing itself as a powerful tool capable of maintaining and strengthening cybersecurity defense systems in large-scale environments and in the appropriate context.

Furthermore, AI is even more important because it can autonomously adapt and learn how data moves and where it is utilized, enabling it to keep pace with changes in corporate workflows not only today but also in the future.

※ Author Introduction
Poornima Apte is a professional writer with an engineering background who specializes in B2B content in robotics, AI, cybersecurity, smart technology, and digital transformation. Her X (formerly Twitter) account is @booksnfreshair.