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.
USES OF AI IN CYBERSECURITY
The use of AI 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.
ADVANTAGES OF AI IN CYBSECURITY
AI presents many advantages and applications in a variety of areas, cybersecurity being one of them. With fast-evolving cyberattacks and rapid multiplication of devices happening today, AI and machine learning can help to keep abreast with cybercriminals, automate threat detection, and respond more effectively than conventional software-driven or manual techniques.
Here are a few advantages and applications of using AI in cybersecurity:
- Detecting New Threats
- Battling Bots
- Breach Risk Prediction
- Better Endpoint Protection
DOWNSIDES OF AI IN CYBSECURITY
The advantages discussed above are just a small chunk of the potential of AI in improving cybersecurity.
However, as with anything, there are also some downsides to using AI in this field. In order to build and maintain an AI system, organizations would need substantially more resources and financial investments.
Furthermore, as AI systems are trained using data sets, you must acquire many distinct sets of malware codes, non-malicious codes, and anomalies. Acquiring all of these data sets is time-intensive and requires investments that most organizations cannot afford.
Without huge volumes of data and events, AI systems can render incorrect results and/or false positives. And getting inaccurate data from unreliable sources can even backfire.
Another major downside is that cybercriminals can also use AI to analyze their malware and launch more advanced attacks, which brings us to the next point.
In recent years, AI has emerged as required technology for augmenting the efforts of human information security teams. Since humans can no longer scale to adequately protect the dynamic enterprise attack surface, AI provides much needed analysis and threat identification that can be acted upon by cybersecurity professionals to reduce breach risk and improve security posture. In security, AI can identify and prioritize risk, instantly spot any malware on a network, guide incident response, and detect intrusions before they start.
AI allows cybersecurity teams to form powerful human-machine partnerships that push the boundaries of our knowledge, enrich our lives, and drive cybersecurity in a way that seems greater than the sum of its parts.