Artificial intelligence (AI) technology has come of age in elevating cyber security. In 2018, global development of AI within the cyber security market reached $7.1 billion, and it’s projected to reach nearly $30.9 billion by 2025.1 It’s a market segment that’s reaching C-suite executives. A Cap Gemini Institute survey of 850 senior information security executives found 61% of enterprises can no longer detect breach attempts without AI technology.2 Another 48% claim they’ll be increasing AI budgets by an average of 29% in FY 2020. Seventy-five percent of these executives say they are currently testing AI cyber security use cases.3
The further along you are in your digital transformation, the greater your exposure to cyberattacks. Digitalization means more entry points beyond the conventional network perimeter, including cloud deployments and connected mobile and IoT devices. Add increased vulnerabilities to an intensified cyber attack environment and it’s clear; modern cyber security can’t be anything less than smart, agile, and manageable. In this paper, we’ll explore how artificial intelligence and machine learning (ML) can bolster your cyber security practices. We’ll discuss how increased intelligence is helping to forge a more proactive cybersecurity approach that allows you to prevent threats before they unleash destructive payloads. AI offers huge opportunities for cyber security. This is because you move from detection, manual reaction, and remediation towards an automated remediation, which organizations would like to achieve in the next three or five years.– Oliver Scherer, CISO, MediaMarktSaturn Retail Group 4
Reinventing cyber security with AI:
Incorporating AI into cyber security is a global, cross-industry endeavor. The chart below shows how virtually all industries are looking to AI for improved security. Telecommunication, manufacturing, and banking rank highest.5 Telecommunication companies use machine learning algorithms to detect fraudulent activity such as theft or fake profiles, and illegal access, among other activities. Algorithms learn “normal” activity, which allows IT security to spot anomalies with huge data sets. Organizations gain near real-time response to suspicious behaviors.6 Similarly, banks are upgrading and overhauling traditional fraud and cyber security threat detection with AI-based technology. Improved anomaly detection that identifies abnormalities in a dataset can speed up fraud detection and prove more cost effective.7 For decades, traditional firewalls with packet filtering, stateful inspection, VPN support, among other capabilities have defended perimeter-based networks. Next generation firewalls include network device filtering capabilities such as advanced threat prevention, anti-virus, URL filtering, intrusion prevention, and other functions. Protecting beyond conventional networks that can include dynamic, multi-cloud environments, and network-connected endpoint and mobile devices offers new challenges.
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