Applying AI to cybersecurity
AI is ideally suited to solve some of our most difficult problems, and cybersecurity certainly falls into that category. With today’s ever evolving cyber-attacks and proliferation of devices, machine learning and AI can be used to “keep up with the bad guys,” automating threat detection and respond more efficiently than traditional software-driven approaches.
At the same time, cybersecurity presents some unique challenges:
- A vast attack surface
- 10s or 100s of thousands of devices per organization
- Hundreds of attack vectors
- Big shortfalls in the number of skilled security professionals
- Masses of data that have moved beyond a human-scale problem
A self-learning, AI-based cybersecurity posture management system should be able to solve many of these challenges. Technologies exist to properly train a self-learning system to continuously and independently gather data from across your enterprise information systems. That data is then analyzed and used to perform correlation of patterns across millions to billions of signals relevant to the enterprise attack surface.
The result is new levels of intelligence feeding human teams across diverse categories of cybersecurity, including:
- IT Asset Inventory – gaining a complete, accurate inventory of all devices, users, and applications with any access to information systems. Categorization and measurement of business criticality also play big roles in inventory.
- Threat Exposure – hackers follow trends just like everyone else, so what’s fashionable with hackers changes regularly. AI-based cybersecurity systems can provide up to date knowledge of global and industry specific threats to help make critical prioritization decisions based not only on what could be used to attack your enterprise, but based on what is likely to be used to attack your enterprise.
- Controls Effectiveness – it is important to understand the impact of the various security tools and security processes that you have employed to maintain a strong security posture. AI can help understand where your infosec program has strengths, and where it has gaps.
- Breach Risk Prediction – Accounting for IT asset inventory, threat exposure, and controls effectiveness, AI-based systems can predict how and where you are most likely to be breached, so that you can plan for resource and tool allocation towards areas of weakness. Prescriptive insights derived from AI analysis can help you configure and enhance controls and processes to most effectively improve your organization’s cyber resilience.
- Incident response – AI powered systems can provide improved context for prioritization and response to security alerts, for fast response to incidents, and to surface root causes in order to mitigate vulnerabilities and avoid future issues.
- Explainability – Key to harnessing AI to augment human infosec teams is explainability of recommendations and analysis. This is important in getting buy-in from stakeholders across the organization, for understanding the impact of various infosec programs, and for reporting relevant information to all involved stakeholders, including end users, security operations, CISO, auditors, CIO, CEO and board of directors.
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