Given the plethora of cyber threats, artificial intelligence (AI) has emerged as a highly valuable domain as it involves leveraging AI to identify and prevent cyber attacks with minimal 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 domain of AI, machines do “intelligent” or “intelligent” things on their own or armed with the appropriate algorithm. Interestingly, it is the interconnected world driven by AI that has become vulnerable to attack. Once again, to combat them, advanced AI security applications are being used to go beyond merely identifying good or bad behavior. These applications analyze large amounts of information and help bring together related activities that may indicate suspicious behavior.
Vulnerability of today's interconnected world
In 2016, hackers turned to various Internet of Things (IoT) devices to create an extensive botnet that they could use to send enough traffic to take down Dyn, the DNS provider. A significant number of security breaches also occurred during 2018 and 2019. All of this should serve as a warning about what could happen on a global scale if organizations do not take the necessary precautions.
Today, we have an entire ecosystem based on data-driven technologies that continually grow in their interconnections. Pertinently, analyst firm Gartner's prediction for 2020 is that 20.4 billion connected things will be in use around the world. With autonomous things becoming a global trend, all these interconnected devices are vulnerable to security breaches. In this context, it becomes essential for IoT manufacturers and their entire supply chain to significantly increase security in all smart products, whether they produce smart and automated refrigerators, robots, drones, vehicles or health trackers.
In light of growing data breaches and cyberattacks, the European Union's General Data Protection Regulation (GDPR) has tightened rules on privacy and data protection laws. It is worrying that emerging technologies like cryptocurrency are still not compliant with privacy laws. Entering personal data into public blockchains requires a high level of security.
With data at greater risk than ever, the world will see an increase in investment, training and education on the cybersecurity front in the coming years. As cybersecurity experts struggle to analyze the enormous amount of data at any given time, artificial intelligence applications such as machine learning and deep learning help them find quick and successful solutions to network problems. cybernetics. After collecting the characteristics related to a problem, machine learning techniques are used in mathematical and statistical terms to extract information from the 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 what we want to detect. In many cybersecurity problems, the threat that needs to be detected is not implicitly defined. Furthermore, obtaining the most up-to-date data is a formidable challenge facing AI in the cybersecurity domain.
Advantages of AI Security
Today, as we know, IoT devices are vulnerable to hackers on a global level. Companies need to come together to form a cybersecure ecosystem and review their views on data analysis. AI helps security operations analysts stay ahead of threats without employing too many resources. It can curate threat intelligence from millions of research articles, blogs, and news stories to respond to hackers based on similar or previous activity. It provides instant insights to help analysts cut through the noise of thousands of daily alerts, significantly reducing response time.
According to the recent Capgemini Research Institute report, AI security is capable of correlating events and triaging them, which again reduces the time required for incident response and remediation. Armed with AI tools, security analysts don't have to struggle to find the time needed to detect new threats. AI helps organizations save on resource-intensive methods of hunting threats, which may also have resulted in alert fatigue. Ultimately, AI reduces the cost of detecting and responding to breaches and threats.
Common AI tools and applications
AI uses learning based on past behavior in a fast, actionable context and provides insights when presented with new or unfamiliar information/behaviors.
AI draws logical, inferred conclusions based on possible subsets of incomplete data. It presents multiple solutions to a known issue to enable security teams to select the best path to remediation.
Although AI applications are constantly growing, some of the most popular include spam filter applications (spamassassin); detection and prevention of network intrusions; fraud detection; credit score and next best offers; botnet detection; secure user authentication; cybersecurity ratings and hacking incident prediction.
To detect whether the software is a malware or not, an AI application determines some distinguishing characteristics of harmless software and some malware for these characteristics. Some of the resources to be used in software analysis include APIs accessed; fields accessed on disk; environmental products accessed (camera, keyboard, etc.); energy consumed from the processor; bandwidth consumed and amount of data transmitted over the Internet. An AI-powered system tries to detect whether the software is malware or not by analyzing these distinctive features.
At the basic level, open source code is used to filter spam emails. It includes analyzing certain features to determine whether an email is spam or not. These extracted features can be processed with Naive Bayes algorithm. This is just a basic example of how AI can be used to detect cyberattacks. Today, many advanced AI applications have been developed to identify cyber attacks. Programming languages like Python and Numpy can be used to perform statistical and mathematically complex calculations with ease. To code the appropriate machine learning algorithm, open source languages can easily access many libraries such as Scikit Learn (Sk-learn) and Pandas which execute the intended algorithm with just four lines of code to process the data quickly it is efficient.
Organizations should be aware of some of the steps some attackers may use. Attackers may leave some traces in some of their steps, or they may access information about the targeted company that was leaked before while they are on an information acquisition spree. Companies can avoid this type of situation if they constantly monitor their public presence under the eyes of an attacker. They need to take precautions to rule out information leaks about their online interface that attackers might find by doing prior research. They often attack a company on shady forums or social media or steal information about a company's customers and employees by scanning most of the information that can be accessed over the Internet (through email, passwords, credit card information, etc.)
Some actors in the field of cybersecurity
Darktrace was founded in 2013. This company developed a product that performs network anomaly detection with machine learning. CYLANCE, founded in 2012, is another prominent name that has developed a product to prevent advanced level of cyber threats.
Leaving these examples aside, riding the wave of artificial intelligence, in recent years we have seen a substantial increase in the number of start-ups focusing on the domain of cybersecurity. According to a CBInsight report, cybersecurity ranks 5th in artificial intelligence applications.
Will AI eliminate jobs in the cybersecurity industry?
It is predicted that we will have 3.5 million unfilled cybersecurity jobs by 2021. AI can indeed fill the talent gap projected by the cybersecurity industry. However, AI alone is not a panacea for all cybersecurity problems. Despite its immense benefits in combating security breaches, companies should not view AI as a cost-effective replacement for cybersecurity workers and an invitation to unnecessary risks.
In the long term, AI security adds value to existing cybersecurity employees' workflows. This can significantly reduce the time required 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 improve security, new threats continue to emerge, with stubborn hackers never giving up trying to find ways to evade even the most stringent security measures. Therefore, AI security needs to continually evolve to defend against cunning hackers, in conjunction with various cybersecurity professionals in their work.
