10 charts that will change your perspective of AI in security
Rapid advances in AI and machine learning are defining cybersecurity’s future daily. Identities are the new security perimeter and Zero Trust Security frameworks are capitalising on AI’s insights to thwart breaches in milliseconds. Advances in AI and machine learning are also driving the transformation of endpoint security toward greater accuracy and contextually intelligence.
69% of enterprise executives believe artificial intelligence (AI) will be necessary to respond to cyberattacks with the majority of telecom companies (80%) saying they are counting on AI to help identify threats and thwart attacks according to Capgemini.
Gartner predicts $137.4bn will be spent on information security and risk management in 2019, increasing to $175.5bn in 2023, reaching a CAGR of 9.1%. Cloud security, data security, and infrastructure protection are the fastest-growing areas of security spending through 2023.
The following 10 charts illustrate the market and technological factors driving the rapid growth of AI in security today:
AI shows the greatest potential for fraud detection, malware detection, assigning risk scores to login attempts on networks, and intrusion detection
Supervised and unsupervised machine learning algorithms are proving to be effective in identifying potentially fraudulent online transaction activity.
By definition, supervised machine learning algorithms rely on historical data to find patterns not discernible with traditional rule-based approaches to fraud detection. Finding anomalies, interrelationships, and valid links between emerging factors and variables is unsupervised machine learning’s core strength.
Combining each is proving to be very effective in identifying anomalous behavior and reducing or restricting access. Kount’s Omniscore relies on these technologies to provide an AI-driven transaction safety rating. Source: Capgemini Research Institute, Reinventing Cybersecurity with Artificial Intelligence – The new frontier in digital security (28 pp., PDF, no opt-in).
80% of telecommunications executives stated that they believe their organisation would not be able to respond to cyberattacks without AI
Across all seven industries studied in a recent Capgemini survey, 69% of all senior executives say they would not be able to respond to a cyberattack without AI. 75% of banking executives realise they’ll need AI to thwart a cyberattack.
59% of utilities executives, the lowest response to this question on the survey, see AI as essential for battling a cyberattack. Utilities are one of the more vulnerable industries to attacks given their legacy infrastructure. Source: Statistica, Share of organisations that rely on artificial intelligence (AI) for cybersecurity in selected countries as of 2019, by industry
51% of enterprises primarily rely on AI for threat detection, leading prediction, and response
Consistent with the majority of cybersecurity surveys of enterprises’ AI adoption for cybersecurity in 2019, AI is relied the majority of the time for detecting threats. A small percentage of enterprises have progressed past detection to prediction and response, as the graphic below shows.
Many of the more interesting AI projects today are in prediction and response, given how the challenges in these areas expand the boundaries of technologies fast. Source: Capgemini Research Institute, Reinventing Cybersecurity with Artificial Intelligence – The new frontier in digital security (28 pp., PDF, no opt-in).
75% of enterprises are relying on AI-based platforms and solutions for network security today, making it the most common use case
71% are relying on AI for data security, and 68% for endpoint security. The graphic below reflects how AI now is integral to the top seven cybersecurity strategies many enterprises rely on today. Source: Statistica, Top Artificial Intelligence (AI) Use Cases For Cybersecurity In Organisations In Selected Countries As Of 2019
Enterprises are relying on AI as the foundation of their security automation frameworks
AI-driven security automation frameworks are designed to flex and support new digital business models across an organisation. Existing security automation frameworks can crunch and correlate threat patterns on massive volumes of disparate data, which introduces opportunities for advanced cybersecurity without disrupting business.
Using alerts and prescriptive analytics for dynamic policies to address identified risks, enterprises can speed deployment of threat-blocking measures, increasing the agility of security operations. Source: Cognizant, Combating Cybersecurity Challenges with Advanced Analytics (PDF, 24 pp., no opt-in).
Cybersecurity leads all other investment categories this year of TD Ameritrade’s Registered Investment Advisors (RIA) Survey
The survey found RIAs are most interested in investment opportunities for their clients in AI-based cybersecurity new ventures. Source: TD Ameritrade Institutional 2019 RIA Sentiment Survey (PDF, 35 pp., no opt-in)
62% of enterprises have adopted and implemented AI to its full potential for cybersecurity, or are still exploring additional uses
AI is gaining adoption in U.S.-based enterprises and is also being recommended by government policy influencers. Just 21% of enterprises have no plans for using AI-based cybersecurity today. Source: Oracle, Security In the Age Of AI (18 pp., PDF. no opt-in)
71% of today’s organisations reporting they spend more on AI and machine learning for cybersecurity than they did two years ago
26% and 28% of U.S. and Japanese IT professionals believe their organisations could be doing more. Additionally, 84% of respondents believe cyber-criminals are also using AI and ML to launch their attacks. When considered together, these figures indicate a strong belief that AI/ML based cybersecurity is no longer simply nice to have; it’s crucial to stop modern cyberattacks. Source: Webroot, Knowledge Gaps: AI and Machine Learning in CyberSecurity Perspectives from the U.S. and Japanese IT Professionals (PDF, 9 pp., no opt-in)
73% of enterprises have adopted security products with some form of AI integrated into them
Among enterprises that receive more than 1,000 alerts per day, the percentage that has AI-enabled products in their security infrastructure jumps to 84%. The findings suggest that some decision makers view AI as useful capability in dealing with the flood of alerts that they receive. Source: Osterman Research, The State of AI in Cybersecurity: The Benefits, Limitations and Evolving Questions (PDF, 10 pp., opt-in).
AI’s greatest benefit is the increase in the speed of analysing threats (69%) followed by an acceleration in the containment of infected endpoints/devices and hosts (64%)
Because AI reduces the time to respond to cyber exploits organisations can potentially save an average of more than $2.5 million in operating costs. Source: The Value of Artificial Intelligence in Cybersecurity – Sponsored by IBM Security Independently conducted by Ponemon Institute LLC, July 2018.
Interested in hearing industry leaders discuss subjects like this and sharing their use-cases? Attend the co-located IoT Tech Expo, Blockchain Expo, AI & Big Data Expo, Cyber Security & Cloud Expo and 5G Expo World Series with upcoming events in Silicon Valley, London and Amsterdam and explore the future of enterprise technology.
- » Apple notes continued enterprise presence as Salesforce partnership goes up a gear
- » Scope AR acquires studio WakingApp as enterprise augmented reality ramps up
- » How automation will help enterprises overcome the cybersecurity skills gap
- » Financial services firms rely on BYOD – so how do they stay secure?
- » What’s new on Forrester’s Zero Trust security landscape in 2019: From theory to integration