AI is Transforming the future of Cybersecurity via these trends
Artificial intelligence (AI) has prevailed to be one of the most powerful and cutting-edge technological advancements in the commercial sector. As an ever-increasing number of enterprises turn digital, companies throughout the globe are continually engineering better approaches to implement AI-based functions into basically every platform and software tool available to them. As a characteristic outcome, however, cybercriminals too are on the rise and view the increasing digitization of business as a lucrative opportunity. It should not shock anyone, then, that AI is influencing cybersecurity — yet it’s impacting it in both positive and negative ways.
The Demand for Cybersecurity Specialists
Cybercrime is a highly opportunistic business and one of the biggest threats to each organization on the globe. According to the Official 2019 Cybercrime Report by Cybersecurity Ventures, cybercrime will cost the world USD 6 trillion annually by 2021, up from USD 3 trillion in 2015. Cybercrime has already damaged both private and public enterprises, driving up data and cybersecurity budgets at small, medium, and large businesses alike, as well as educational institutions, organizations, and governments all around the world. In fact, the report also anticipates that global spending on cybersecurity products and services will surpass USD 1 trillion in total from 2017 to 2021 — a 12% to 15% YOY market growth over the period.
In that capacity, cybersecurity experts are in high demand; cybercrime is expected to significantly triple the number of employment opportunities to 3.5 million unfilled cybersecurity positions by 2021, up from 1 million in 2014, with the sector’s unemployment rate staying at 0%.
This extreme employee setback is creating lucrative opportunities for AI solutions to help automate threat detection and response. Cybersecurity experts are some of the most hardworking workers around because of the extent of strained resources. AI can ease the burden, robotize dull and tedious tasks, and possibly help identify threats more effectively and proficiently than other software-driven approaches.
How AI Is Enhancing Cybersecurity
In fact, cyber threat detection is one of the areas of cybersecurity where AI is picking up the most traction and proving to be the most beneficial. Machine learning-based advancements are especially efficient at detecting unidentified threats to a system. Basically, ML is a part of AI where computers use and train algorithms relying upon the data received, as well as improve. In the world of cybersecurity, this translates into a machine that can anticipate dangers and identify anomalies with high precision and speed than a human would have done — despite using the most developed non-AI software tool.
This is a stamped improvement over traditional cybersecurity systems, which depend on principles, threat intelligence, and signatures for identifying dangers and responding to them. Nonetheless, these systems are basically past-centric and are programmed around what is already known about previous attacks and attackers. The issue here is that cybercriminals can make new and imaginative attacks that manipulate the characteristic of vulnerable sides in the different systems. Furthermore, the sheer volume of security alerts an organization needs to manage every day is often a lot for resource-stretched security teams to deal with when relying on traditional security technology and human expertise alone.
Innovations in AI, nonetheless, have resulted in the production of a lot more brilliant and autonomous security systems. With ML applied, a significant number of these systems can learn for themselves without the assistance of a human equivalent (unsupervised), and keep pace with the measure of data that security systems generate. ML algorithms are incredibly great at recognizing anomalies in patterns. Instead of searching for matches with particular signatures — a conventional tactic that present-day attacks have all but rendered pointless — the AI system first standardizes what is normal, and from that point, dives deep into what anomalies could arise to detect threats.
As opposed to searching for matches with explicit marks — a conventional strategy that current assaults have everything except rendered pointless — the AI framework first makes a gauge of what is ordinary, and from that point, plunges profound into what strange occasions could jump out at identifying assaults.
Today, an ever-increasing number of organizations depend on automation, AI, and ML to robotize threat detection. As per Cisco’s 2018 Security Capabilities Benchmark Study, 39% of companies totally depend on automation to detect any cyber anomalies, while 34% depend on ML, and 32% completely depend on AI.
How Is AI Getting Exploited
In the latest report dubbed ‘The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation,’ a board of 26 professionals from the US and UK identify numerous occasions where AI can be weaponized and used to boost and upscale cyberattacks.
Probably the biggest threat mentioned is that AI can be used to robotize attacks on a truly gigantic scale. Attackers generally depend on workforces of their own to direct attacks. However, by using AI and different instances of ML-powered bots, things like IoT botnets will turn into a much bigger threat. Also, the expenses of attacks can be brought down by the adaptable use of AI systems to finish tasks that would somehow require human intervention, insight, and skill. Since AI may probably offer a solution to the cybersecurity talent shortage, it may also offer a solution for talent shortages in the cybercriminal world.
Advancements in AI will likewise enable a new category of attacks, as per the report. These attacks may use AI systems to finish certain tasks more effectively than any human could, or exploit vulnerabilities that AI frameworks have. For instance, voice is presently being used as an identification method to a great extent. In any case, there have recently been advancements in speech synthesis systems that learn how to emulate people’s voices, and it’s feasible that these systems could be used to hack into frameworks secured by voice authentication. Few other examples include misusing the vulnerabilities in AI systems used in things such as autonomous cars, or even military intelligence.
There is additionally the real possibility of attackers taking advantage of the vulnerabilities of AI-based security defense systems. For instance, speaking of supervised ML, an attacker could possibly access the training data and switch names, so some malware examples are labeled as bug-free code by the system, invalidating its defenses.
The Bottom Line
AI is surely something of a two-edged sword with regards to security. While solutions that use AI and ML can significantly reduce the amount of time required for threat detection and incidence response, the technology can likewise be used by cybercriminals to boost the proficiency, scalability, and success rate of attacks, definitely modifying the threat landscape for organizations in the years to come. Unquestionably, AI will prove to be beneficial to cybersecurity in the years ahead — and it should be, on the grounds that AI is likewise opening up totally different classes of attacks that organizations should be outfitted to deal with very soon.
