Uncovering AI’s Role in Cybersecurity - AI in Cybersecurity

Posted on : July 12th 2023

The AI Revolution is Making Cybersecurity More Precise

The latest AI and data practices have bolstered cybersecurity by automating several tasks and enhancing threat detection procedures. The introduction of various new technologies like cloud, IoT, etc., has brought along new cybersecurity threats. Cyberthreats are now more sophisticated and elusive, prompting companies to adopt AI and data strategies to identify anomalies faster and enable real-time monitoring of intrusions

Exhibit 1: AI Use Cases in Cybersecurity

Source: NordLayer

AI’s adaptive capabilities make it well-equipped to address evolving security challenges, protect enterprise assets, and ensure data integrity. According to Gartner, by 2025, 60% of organizations will consider cybersecurity risk assessment as a crucial factor in conducting third-party transactions and commercial engagements. Gartner also estimates that by 2025, 80% of enterprises will adopt a strategy to consolidate web, cloud services, and private application access through a single vendor’s security service edge (SSE) platform.

Exhibit 2: Benefits of AI in Cybersecurity

Source: Straive

AI tools can provide adversarial learning opportunities by intentionally probing cybersecurity defenses to detect weaknesses. Such scenario testing can help uncover vulnerabilities that external malicious entities could potentially exploit.

Tapping AI’s Wizardry

AI combines extensive data sets with cognitive processing algorithms, integrating deep learning and machine learning to provide versatile solutions.

Exhibit 3: Use cases of AI in Cybersecurity

Source: Straive

In the realm of cybersecurity, AI provides several advantages as its algorithms continuously learn by consuming volumes of data, enabling faster threat detection and the identification of relationships between systems and data artifacts. This helps human operators to triage security events and make critical decisions quickly. Typically, AI/ML algorithms learn patterns from input data and can flag anomalies beyond the expected pattern range, such as unexpected file movements. This enables the timely issuance of focused alerts, reducing false positives and conserving resources.

However, it is important to note that AI can also be a potent tool in the hands of smart cybercriminals, potentially compromising enterprise systems as well.

Exhibit 4: AI-powered Cybersecurity Attacks

Source: Morgan Stanley

Preventing AI-powered attacks is a challenging task that necessitates the use of tools and skills to consistently identify and address potential weaknesses in an AI model’s ability to detect and prevent cybersecurity attacks. Real-time AI-based threat detection at scale not only expedites digitization but also provides enhanced support for remote workforce. It is a game-changer, offering stronger cybersecurity protection at lower costs and on a larger scale.

A Whole-of-Enterprise Approach

Cyber threats have become increasingly complex and sophisticated, with AI reinforcing these threats through tactics like social engineering. AI-powered tools are necessary to combat AI-powered attacks. Ultimately, the decision to use AI for cybersecurity is a business decision that requires the implementation of comprehensive governance architectures to support end-to-end adoption and overview.

Therefore, it is essential to assess or audit current cybersecurity capabilities and the future vision of an enterprise, while also considering where improvements are essential or feasible. Developing a roadmap that balances business objectives and risk appetite serves as a solid starting point for strategizing the integration or reinforcement of AI for cybersecurity purposes.