How AI Is Changing the Cybersecurity Landscape
Artificial intelligence (AI) is proving to be one of the most influential and game-changing technology advancements in the business world. As more and more enterprises go digital, companies all over the globe are constantly engineering new ways to implement AI-based functions into practically every platform and software tool at their disposal. As a natural consequence, however, cybercriminals too are on the rise, and view the increasing digitization of business as wide open window of opportunity. It should come as no surprise, then, that AI is affecting cybersecurity – but it’s affecting it in both positive and negative ways.
Cybercrime is a massively lucrative business, and one the greatest threats to every company in the world. Cybersecurity Ventures’ Official 2019 Annual Cybercrime Report predicts cybercrime will cost the world $6 trillion annually by 2021 – up from $3 trillion in 2015. Cybercrime is creating unprecedented damage to both private and public enterprises, driving up information and cybersecurity budgets at small, medium and large businesses alike, as well as governments, educational institutions, and organizations of all types globally. Indeed, CV’s Cybersecurity Market Report also forecasts that global spending on cybersecurity products and services will exceed $1 trillion cumulatively from 2017 to 2021 – a 12% to 15% year-over-year market growth over the period.
As such, cybersecurity professionals are in high demand – cybercrime is expected to triple the number of job openings to 3.5 million unfilled cybersecurity positions by 2021, up from 1 million in 2014, with the sector’s unemployment rate remaining at 0%.
This drastic employee shortfall is creating an opportunity for AI solutions to help automate threat detection and response. With such strained resources, security professionals are some of the most hardworking employees around. AI can ease the burden, automate repetitive and tiresome tasks, and potentially help identify threats more effectively and efficiently than other software-driven approaches.
Cyber threat detection is in fact one of the areas of cybersecurity where AI is proving the most useful and gaining the most traction. Machine learning-based technologies are particularly efficient at detecting unknown threats to a network. Machine learning is a branch of AI where computers use and adapt algorithms depending on the data received, learn from this data, and improve. In the realms of cybersecurity, this translates into a machine that can predict threats and identify anomalies with greater accuracy and speed than a human equivalent would be able to – even one using the most advanced non-AI software system.
This is a marked improvement over conventional security systems, which rely on rules, signatures and threat intelligence for detecting threats and responding to them. However, these systems are essentially past-centric, and are built around what is already known about previous attacks and known attackers. The problem here is that cybercriminals are able to create new and innovative attacks which exploit the inherent blind spots in the various systems. What’s more, the sheer volume of security alerts a company has to deal with on a daily basis is often too much for resource-stretched security teams to handle when relying on conventional security technology and human expertise alone.
Advancements in AI, however, have led to the production of much smarter and autonomous security systems. With machine learning applied, many of these systems can learn for themselves without the need for human intervention (unsupervised), and keep pace with the amount of data that security systems produce. Machine learning algorithms are exceptionally good at identifying anomalies in patterns.
