The State of AI in Cybersecurity 2023: Insights About Use Cases
How AI in cybersecurity prioritizes process automation, enhancing efficiency and mitigating risks.
In Aberdeen’s recent study on The State of AI in 2023, we found that all three of the traditional use cases for AI in cybersecurity are prevalent:
- Pattern recognition: illustrative examples include filtering out noise, minimizing false positives, and freeing up cybersecurity analysts to work on the most relevant threats.
- Process automation: illustrative examples include analyzing and correlating data from multiple sources and enabling faster triage, forensics, and response to security-related incidents.
- Predictions: illustrative examples include automatically scanning networks and systems for vulnerabilities, identifying weaknesses most likely to be exploited by attackers, and prioritizing recommended patches or updates.
However, the top performers — so far, at least — are strongly differentiated from all others by an extra emphasis on process automation, as seen in Figure 1 below. Saving time and money is always welcome, but in this case, they can also lead directly to other highly desirable outcomes — including reducing security-related risks and acting as a force multiplier to help address the endemic cybersecurity workforce shortage.
Figure 1: All three traditional use cases for AI in cybersecurity are prevalent, but the top performers have an extra emphasis on process automation
Source: The State of AI in 2023; Aberdeen, October 2023
See More: The State of AI in Cybersecurity 2023: The Good News — and Some Ongoing Challenges
In addition, Aberdeen’s State of IT in 2023 dataset shows that all three of the traditional high-level categories of business value for cybersecurity-related initiatives were among the top benefits that organizations say they have realized as a result of using AI in a cybersecurity context:
- Improve operational efficiencies — e.g., reduce cost and time, increase productivity (51% of all respondents)
- Manage downside risks to a more acceptable level — e.g., reduce the frequency and impact of security-, privacy-, fraud-, and compliance-related incidents (43% of all respondents)
- Enable/optimize the success of upside opportunities and strategic business objectives — e.g., increase revenue, growth, profitability, and market share (38% of all respondents)
In this regard, there was little difference between the top performers and all others.
TL;DR — The Main Takeaway
Aberdeen’s research shows that organizations already recognize that AI can contribute to their cybersecurity-related initiatives in multiple dimensions — but right now, saving time and money by emphasizing process automation use cases is given the highest priority.
In Aberdeen’s view, this approach is well-suited for our current economic times and the current state of maturity for AI in cybersecurity.
In the final blog of this 4-part series about The State of AI in Cybersecurity, we’ll look at Aberdeen’s research findings about the leading AI-enabled solution categories, both current and planned.
How can your organization leverage AI for cybersecurity? Why is it crucial in today’s cybersecurity landscape? Let us know on Facebook, X, and LinkedIn. We’d love to hear from you!
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