IBM’s Cost of a Data Breach Report 2024 highlights a ground-breaking finding: The application of AI-powered automation in prevention has saved organizations an average of $2.2 million.
Enterprises have been using AI for years in detection, investigation and response. However, as attack surfaces expand, security leaders must adopt a more proactive stance.
Here are three ways how AI is helping to make that possible:
1. Attack surface management: Proactive defense with AI
Increased complexity and interconnectedness are a growing headache for security teams, and attack surfaces are expanding far beyond what they can monitor using manual means alone. As organizations level up their multi-cloud strategies and onboard new SaaS tools and third-party code in software development and deployment, the challenge only intensifies.
With these larger attack surfaces come increased complexity of network interactions and many new potential entry points for adversaries to exploit. Attack surface management (ASM) brings AI-powered, real-time protection to digital infrastructures, regardless of underlying complexity.
Automated ASM greatly augments manual auditing by providing comprehensive visibility into attack surfaces. Furthermore, AI learns from the data it monitors to Improve future detection outcomes, albeit at a speed and scale that humans alone can’t match.
However, while ASM tools are often presented as turnkey solutions and are usually relatively easy to deploy, the ability of security teams to interpret the huge influx of data they generate is essential for maximizing their impact.
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