Given the plethora of cyber threats, artificial intelligence (AI) has emerged as a highly valuable domain as it involves leveraging AI to identify and stop cyber-attacks with a minimum of human intervention. AI tools and techniques autonomously identify, discover, predict, justify, act, and learn about potential cybersecurity threats without needing much human intervention.
In the AI domain, machines do “smart” or “intelligent” things on their own or armed with the appropriate algorithm. Curiously, it is the interconnected world driven by AI that has become vulnerable to attacks. Again, to counter them, advanced applications of AI security are being used to go beyond merely identifying good or bad behavior. These applications analyze vast amounts of information and help to piece together a related activity that could indicate suspicious behavior.
Vulnerability of today’s interconnected world
In 2016, hackers turned to several Internet of Things (IoT) devices to create an extensive botnet which they could use to push enough traffic to take down Dyn, the DNS provider. A significant number of security breaches occurred during 2018 and 2019 too. All this should serve as an alert of what can happen at a global scale if organizations don’t take necessary precautions.
Today, we have an entire ecosystem based on data-driven technologies that are continually growing in its interconnections. Pertinently, analyst firm Gartner’s forecast for 2020 is that 20.4 billion connected things will be in use worldwide. With autonomous things becoming a global trend, all these interconnected devices are vulnerable to a security breach. In this context, it becomes paramount for IoT manufacturers and all of their supply chain to significantly increase security in all intelligent products, whether they produce smart, automated refrigerators, robots, drones, vehicles, or health trackers.
In the light of growing data breach and cyber-attacks, the European Union’s General Data Protection Regulation (GDPR) has tightened rules on privacy and data protection laws. It is a matter of concern that emerging technologies like cryptocurrency are yet to be compliant with privacy laws. Insertion of personal data into public blockchains requires a high level of security.
With data at higher risk than ever, the world is bound to see an increase in investment, training, and education on the cybersecurity front over the next few years. As cybersecurity experts struggle to analyze the tremendous amount of data in a certain time, artificial intelligence applications such as machine learning and deep learning help them come up with successful and fast solutions to cyber network concerns. After collecting the features related to a problem, the machine learning techniques are used in terms of mathematical and statistical ways to extract information from data and subsequently guess the unknown threat. Of particular help is a deep learning algorithm that uses artificial neural networks.
However, these applications have to go beyond defining the things that we want to detect. In many cybersecurity problems, the threat that needs to be detected is not implicitly defined. Also, getting the most updated data is a formidable challenge that confronts the AI for the cybersecurity domain.
Advantages of AI security
Today, as we know, IoT devices stand vulnerable to hackers at the global level. Businesses need to come together to form a cyber-secure ecosystem to review their take on data analytics. AI helps security operations analysts stay ahead of threats without employing too many resources. It can curate threat intelligence from millions of research papers, blogs, and news stories to respond to hackers based on similar or previous activity. It provides instant insights to help analysts fight through the noise of thousands of daily alerts, significantly reducing response time.
According to the Capgemini Research Institute’s recent report, AI security is capable of correlating events and triaging them, which again cuts down on time needed for incident response and remediation. Armed with AI tools, security analysts need not struggle to find the time required to detect new threats. AI helps organizations in saving on resource-intensive methods for threat hunting, which might have resulted in alert fatigue too. Ultimately, AI lowers the cost to detect and respond to breaches and threats.
Common tools and applications of AI
AI makes use of learning based on past behavior in quick, actionable context and provides insights when presented with new or unknown information/behaviors.
AI makes logical, inferred conclusions based on potential incomplete subsets of data. It presents multiple solutions to a known problem to empower security teams to select the best path towards remediation.
Though AI applications are ever-growing, some of the popular ones include spam filter applications (spamassassin); network intrusion detection and prevention; fraud detection; credit scoring and next-best offers; botnet detection; secure user authentication; cybersecurity ratings, and hacking incident forecasting.
To detect whether the software is a malware or not, an AI application determines some distinctive features of harmless software and some malware to those features. Some of the features to use in the analysis of software include accessed APIs; accessed fields on the disk; accessed environmental products (camera, keyboard, etc.); consumed processor power; consumed bandwidth, and amount of data transmitted over the internet. An AI-powered system tries to detect whether the software is a malware or not by analyzing these distinguishing features.
At the basic level, open-source code is used to do spam mail filtering. It includes analysis of certain features to determine whether an email is a spam mail or not. Such extracted features can be processed with the Naive Bayes algorithm. This is just a basic example of how AI can be used to detect cyber-attacks. Today many advanced AI applications have been developed to identify cyber-attacks. Programming languages such as Python and Numpy can be used to achieve statistical and mathematically complex calculations with ease. To code the appropriate machine learning algorithm, open source languages can easily access a lot of libraries such as Scikit Learn (Sk-learn) and Pandas that make running the intended algorithm with just four lines of code to process the data fast and effectively.
Organizations must know some of the steps that some attackers may use. Attackers might leave some traces in some of their steps, or they can access information about the targeted company that was leaked before, while they are on an information acquisition spree. Companies can prevent these kinds of situations if they keep track of their public presence constantly with the eyes of an attacker. They need to take precautions to rule out the leakage of information about their online interface that attackers might come across while doing their research beforehand. They often target a company in dark forums or social media or steal information about a company’s customers and employees by scanning most of the information that can be accessed via the internet (through e-mail, passwords, credit card information, etc.)
Some players in cybersecurity domain
Darktrace was founded in 2013. This company has developed a product that does anomaly detection on a network with machine learning. CYLANCE, founded in 2012, is another prominent name that has developed a product to prevent the advanced level of cyber threats.
These examples apart, riding on the wave of artificial intelligence, recent years have seen a substantial increase in the number of start-ups that focus on the cybersecurity domain. According to a CBInsight report, cybersecurity features at the 5th place in the applications of artificial intelligence.
Will AI take away jobs in cybersecurity industry?
It is predicted that we will have 3.5 million unfilled cybersecurity jobs by 2021. AI can indeed fill the cybersecurity industry’s projected talent gap. Yet, AI in isolation is not a panacea for all cybersecurity problems. Despite its immense benefits in countering security breaches, enterprises should not consider AI as a cost-effective replacement of cybersecurity workers and invite unnecessary risks.
In the long run, AI security does value addition to the workflows of existing cybersecurity employees. It can significantly cut down their time needed for threat hunting, alert triage, or correlation; they can then focus on other essential tasks that cannot be automated through AI.
Given the evolving nature of technology to enhance security, newer threats keep on emerging with dogged hackers never giving up attempts to find ways to sneak through even the most stringent security measures. So, AI security has to continually evolve to defend against the wily hackers in tandem with numerous cybersecurity professionals at their job.
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