Notes

Artificial intelligence (AI), also known as machine intelligence, is a branch of computer science that focuses on building and managing technology that can learn to autonomously make decisions and carry out actions on behalf of a human being.

AI is not a single technology. Instead, it is an umbrella term that includes any type of software or hardware component that supports machine learning (ML), computer vision (CV), natural language understanding (NLU), natural language generation, natural language processing (NLP) and robotics
Today’s AI uses conventional CMOS hardware and the same basic algorithmic functions that drive traditional software. Future generations of AI are expected to inspire new types of brain-inspired circuits and architectures that can make data-driven decisions faster and more accurately than a human being can.
Today’s AI uses conventional CMOS hardware and the same basic algorithmic functions that drive traditional software. Future generations of AI are expected to inspire new types of brain-inspired circuits and architectures that can make data-driven decisions faster and more accurately than a human being can.
Artifical intelligence in cyber security;
is rapidly permeating the enterprise security ecosystem, bringing a wide range of advanced capabilities to what is quickly becoming a key aspect of a successful digital business model
But while it might be tempting to just throw AI at the digital wall to see where it sticks, wiser business leaders are taking the time to ascertain where it can be used most effectively and how it must blend with normal operations so as not to hamper overall performance.

According to Statista, the most popular security applications for AI involve protecting the network, endpoints and the data itself. Upwards of 75 percent of IT executives surveyed in 2019 report networking as the top area of concern, followed by 71 percent for data security and 68 percent for endpoints
AI brings a number of powerful tools to these particular applications, but perhaps none greater than its ability to sift through extreme amounts of data looking for patterns that indicate either a potential or actual security breach. As well, AI is finding its way into areas like ID and access management, and protecting assets that are increasingly being provisioned outside the traditional firewall: in the cloud and the internet of things (IoT) edge
Not All AI is Created Equal
Still, enterprise executives should be wary of lumping all forms of artificial intelligence into one broad category. The fact is there are many different flavors of AI, each of which brings unique capabilities to cybersecurity.

Machine learning (ML), for instance, has shown itself to be highly effective in areas like threat detection, attack mitigation and mobile device security. Fintech News’ Chandni Naidu notes that this is due to ML’s ability to adapt and change to evolving circumstances without the need for human intervention.

What’s more, she says, as data environments become more complex, ML can more easily assume the many rote, mundane aspects of security, leaving human experts to concentrate on the more intuitive, strategic aspects of the job. This can be particularly effective when thwarting DDoS attacks, which try to bring down systems by bombarding them with requests from perhaps thousands of computers. Before Amazon reported sustaining a 2.3 terabits per second (Tbps) DDoS attack in Feb 2020, the largest attack on record was reported by GitHub in 2018 when more than 1.35 Tbps hit the service over a period of 18 minutes.

AI is also emerging as a crucial asset in the development of cybersecurity software. Under the new DevOps model of development, AI can be used to assess vulnerabilities and update code at a rapid pace. This allows organizations to push out new layers of protection and new patches to existing vulnerabilities as fast as new threats arise
What’s more, she says, as data environments become more complex, ML can more easily assume the many rote, mundane aspects of security, leaving human experts to concentrate on the more intuitive, strategic aspects of the job. This can be particularly effective when thwarting DDoS attacks, which try to bring down systems by bombarding them with requests from perhaps thousands of computers. Before Amazon reported sustaining a 2.3 terabits per second (Tbps) DDoS attack in Feb 2020, the largest attack on record was reported by GitHub in 2018 when more than 1.35 Tbps hit the service over a period of 18 minutes.

AI is also emerging as a crucial asset in the development of cybersecurity software. Under the new DevOps model of development, AI can be used to assess vulnerabilities and update code at a rapid pace. This allows organizations to push out new layers of protection and new patches to existing vulnerabilities as fast as new threats arise