How AI can improve how you assess the cyber risk of your organization

How AI can improve how you assess the cyber risk of your organization

The past decade has seen AI emerge as a breakout star in the technology domain. With an ever-growing list of real-world applications, AI has managed to cultivate a staunch following that swears by all that AI can do for the world. With the pandemic in full swing and cybercrime on remote employees on the rise, there's never been a better time to introduce AI into crucial areas such as cybersecurity.

The phenomenal shift of the IT work environment to hybrid or remote operations has called into question the use of traditional malware detection solutions. While we can continue to use these solutions, we need to introduce enhanced ways to defend our networks. And this is where AI can help. Apart from having some very salient use cases such as improving network threat detection and building user behavior models for anomaly detection, AI can usher in far quicker, easier, and more accurate ways to assess the cyber risk companies face.

What AI-based cyber risk quantification does for your company

Uses empirical data to create a forward-looking cyber risk score: With AI-enhanced cybersecurity, an organization can receive an overall cybersecurity risk score based on macro-level factors such as network devices being used and micro-level score factors like IP addresses.

Provides an exhaustive assessment of risk: Based on important parameters such as risk signals, threat levels, organizational and business priorities, historical data, and internet-facing assets, AI can generate accurate, real-time calculations of your company's risk exposure. This assessment give CISOs a better understanding of what data and assets need to be protected and what kind of technology and security policies they need to set up to defend their networks.

Relies on advanced modeling techniques: AI relies on ML-based probabilistic models like the random forest model and recursive neural network to provide multifaceted intelligence on the company's security posture. These models can be trained to analyze historical data, user behavioral factors, and network vulnerabilities and can derive correlations between these parameters and the current security configurations of the company.

Leverages customized reporting: AI-based cybersecurity solutions can also generate security reports that are customized to suit each stakeholder's needs. For example, a security report for a CISO containing more technical aspects about the company's security infrastructure would differ from a report focused on how the current security posture is affecting business—a report that is more suited for a CEO.

Assesses third-party risks: Cyber risks posed by third-party vendors can also affect the security of a company. To mitigate third-party risks, AI-based solutions can take third-party risk factors into account to calculate overall risk scores. An interesting way AI-based solutions do this is by applying Google's method of ranking sites in its search engine results page. One of Google's ranking factors is the number of backlinks (external links that reference or link back to a page) linked to the page. AI-based solutions can also evaluate the number of third parties linked to your organization, the number of assets and amount of data these third parties have access to, and the security levels of the vendors to determine just how much risk the organization faces with respect to external partners.

The use cases for AI in cybersecurity grow more every day thanks to its ability to optimize security mechanisms and reduce the workload on security analysts. Despite AI-enhanced security being pricey right now, its broad spectrum of capabilities can save you all the money you might end up spending on repairing the damage that traditional cybersecurity solutions failed to prevent.

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