The term ‘artificial intelligence’ (AI) was first coined by researchers of the Dartmouth Summer Research Project on Artificial Intelligence in 1956. Artificial Intelligence (AI) and Machine Learning (ML) are buzzwords frequently associated with topics such as Big Data, analytics, and broader technological changes. The terms are used interchangeably, but are not identical. Artificial Intelligence is the overarching concept of machines performing tasks that could be considered intelligent and smart. Machine Learning is a current concept of AI based on the idea that machines should be given access to data and learn for themselves.
Artificial Intelligence is not a new notion and is seen in the Greek myths, for example, as mechanical men such as Talos, designed to mimic human behaviour. Additionally, in the early development of computers in Europe, engineers attempted to create mechanical brains and considered them as “logical machines” with capabilities such as basic arithmetic and memory. Currently, rather than increasingly complex calculations, the field of AI is focusing on mimicking human decision-making processes and carrying out tasks in more human ways.
Artificial Intelligences can be classified into one of two essential groups – applied or general. Applied AI systems are increasingly designed and adopted to trade stocks and shares intelligently and to maneuver an autonomous vehicle and other automation benefits, ranging from predictive outcomes to advanced security options. Although AI encompasses many different technologies, the interest in AI for cybersecurity is being driven by the newfound wealth of data and analysis methods.
Digital trust as a top priority to build and maintain the IT infrastructure for digital transformation
With data breaches becoming more common and day-to-day IT security operations facing greater challenges, losing digital trust can have a substantial impact on brand reputation and the bottom line of most organisations. Consequently, ensuring ‘digital trust’ is a top priority to build and maintain the IT infrastructure for digital transformation. According to a market study by Frost and Sullivan, AI-based cybersecurity can enhance the capabilities of IT staff and help organisations thwart cyber threats. AI and ML are changing the security landscape and are used widely by both hacking and security cybersecurity industries and communities. In a more sophisticated environment coupled with the proliferation of AI-driven attacks in number and frequency, the cost of threat detection and response is escalating. The result is that security professionals need more advanced, smart and automated technologies to combat automated attacks.
While the benefits for consumers of a prioritised digital transformation and connectivity include convenience, efficient services and better experiences, the complications are increased potential risks of cyberattacks on both companies and users. Hence, cybersecurity professionals are leveraging AI and machine learning technologies for responding to the evolving cyber threats faced by individuals, businesses and governments.
Cybercriminals are also using more sophisticated methods to attack organisations
A shortage in trained staff and cybersecurity expertise has left many companies struggling to develop stalwart techniques to counter the polymorphic malware, AI and other automated methods used by cybercriminals. These cumulative challenges in security operations indicate the need for a smarter, more adaptable, automated and predictive security strategy. Subsequently, security companies are developing AI and ML algorithms to deepen their competitiveness, empower security products and augment the capabilities of existing IT and cybersecurity staff.
AI and ML are being incorporated into all stages of cybersecurity
In an effort to implement proactive and automated tactics toward cyber defence, companies are merging AI and ML with strategies to match the whole spectrum of cyber defence including threat prevention or protection, threat detection or hunting, and threat response to predictive security. Smaller technology companies have been the most proactive in introducing multiple AI-enabled security offerings into the market, while larger IT vendors have also incorporated AI and ML into their existing enterprise security solutions.
Key trends in the growth of AI and ML applications
Amy Lin, industry analyst at Frost & Sullivan indicate that; “With cybersecurity solutions powered by AI capabilities, vendors can better support enterprises and their cybersecurity teams with less time and manpower investment and higher efficiency to identify the cybersecurity gaps”. Hence, cybersecurity vendors are integrating AI and ML strategies into solutions to address critical threats related to the hyper-connected workplace, with faster threat detection, mitigation, and response capabilities.
Key AI and ML market trends for cybersecurity include:
- The implementation and inclusion of AI-enabled competences into existing solutions to deepen their competitive advantage;
- Adopting an all-inclusive cybersecurity framework ranging from detection to response and additional prediction; and
- Enabling cybersecurity expert teams to be proactive and reducing lower false-positive rates
In terms of cybersecurity, a system capable of learning results is an automated threat detection and management system that continuously self-improves to be efficient and effective. Additionally, vendors are investigating concepts such as blockchain to reinforce the capabilities of their cybersecurity solutions and services, thus offering better protection and remediation consumers.
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