Artificial intelligence in cybersecurity: applications & the future
December 26, 2022
In recent years, artificial intelligence (AI) has become essential in cyber security. As the threat environment has evolved, so has the need for AI services and cyber security solutions to help organizations defend against new and increasingly sophisticated attacks. Cybersecurity is a complex and ever-changing field, and AI can provide the speed and agility needed to handle emerging threats.
AI in cybersecurity: a summary
While the use of AI in cybersecurity is a decade-old conversation, the recent advancements in computing power and cloud technologies have finally made AI feasible for threat intelligence. In this article, we discuss the role of AI in cybersecurity, outline its most important applications, provide real-life examples and consider the future outlook of this technology.
The future of AI in cybersecurity
CAGR of AI in the cybersecurity market from 2022 to 2033
Acumen Research and Consulting
the global average cost of a single data breach
of executives consider AI the best technology to counter nation-state cyberattacks
AI vs data analytics
To better understand why exactly AI is the most effective technology for combating cyberthreats, let’s compare this technology to its closest relative – data analytics.
By its nature, AI is designed to learn and evolve. This means that it can keep pace with the ever-changing landscape of cybersecurity threats. Additionally, AI can process large amounts of data much faster than humans to quickly identify patterns and anomalies indicating a potential threat.
Supervised by humans, data analytics examines large data sets and identifies patterns, trends or behavior that can point to malicious activity. On the downside, data analytics can be time-consuming and expensive and is limited to human ability to process and interpret data. Most importantly, data analytics systems are highly dependent on humans to evolve, meaning that, unlike AI, such systems are not self-learning.
6 use cases of AI in cyber security
1 Threat detection
Artificial intelligence can identify and prioritize new, previously unknown cyberthreats by analyzing data sets for patterns and anomalies. This allows organizations to respond quickly and efficiently to new threats. In addition, AI can correlate different data sets to provide a complete picture of an attack, so that security teams can understand its scope and nature and better mitigate it in the future. By comparison, traditional methods of security analysis often involve security specialists manually sifting through large data sets, which can be time-consuming and lead to errors.
2 Automated malware detection
Malware is software designed to cause disruption in organizations’ networks by exploiting connected devices. Traditional malware detection methods involve monitoring the network for signature matches, which requires a considerable amount of manual work from security experts. In comparison, AI systems can automatically pinpoint even zero-day malware by analyzing huge amounts of historical data. Moreover, AI can identify patterns and trends in past attacks and alert companies, helping to improve the existing security strategy and proactively protect corporate systems from such future exploits.
3 Social engineering detection
Verizon's 2021 Data Breach Investigation report observes that 85% of cyber security attacks are now social engineering attacks that prey upon humans’ trusting nature. Previously, the problem with using AI for social engineering attacks was that they couldn’t be fully “explained” to ML systems. However, deep learning models, the most sophisticated machine learning models that mimic human thinking, can analyze even unlabeled data and learn independently. This makes deep learning-based systems more effective in combating social engineering attacks than traditional Secure Email Gateway and similar systems. Additionally, AI can also be used to create “phishing simulations” that can test user susceptibility to social engineering attacks. This is a great way of raising awareness and ensuring that employees are better equipped to deal with real-life attacks.
4 Helping to work smarter
Even for seasoned cybersecurity professionals, one of the biggest pain points is incident response prioritization. According to research by Trend Micro Incorporated, 70% of employees at security operation centers are emotionally overwhelmed by security alert volumes. And while AI can’t replace cybersecurity professionals, it can streamline their work and make it easier. Today’s AI models can keep track of the constantly evolving cyber threat landscape and help human analysts decide what security alert to respond to first. As a result, AI can help enterprises allocate their efforts to the most critical threats first, reducing the overall risk to the organization.
5 Managing vulnerability
As the world switched to remote working, old cybersecurity challenges have become more prevalent while new ones arose as well. However, traditional vulnerability assessment tools have grown outdated in increasingly more hybrid environments often containing mobile and IoT devices. The most important distinction between AI-based and conventional vulnerability assessment tools is that the former can accurately define the risk score of all devices, regardless of how critical they are to running a given business. There are situations when even the most isolated devices without an internet connection can pose a significant threat to the organization’s well-being, and AI can provide additional context for assessing their vulnerability.
6 Spotting cyberattack trends
Prolific marketing specialists are already relying on various AI-driven tools to analyze social media trends and learn what topics customers engage with the most. Similarly, AI can be used to analyze forums, cybersecurity social media pages, and news websites to better understand which cyberattacks are becoming more popular in a given sector, and what attacks cybersecurity experts currently deem the most concerning.
Performing such large-scale analysis on a daily basis is almost impossible for human analysts, but by applying modern natural language processing techniques, companies can grasp valuable insights from an endless information stream on the internet.
Real-life examples of AI cyber security adoption
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It is tempting to add machine learning tools into many business processes, and it is indeed becoming ubiquitous, but AI tools suffer from bias, vulnerability to attack, and a lack of explainability. Without proper governance and oversight, we are simply exposing industry, people, and the environment to significantly more risk.
How cybercriminals use AI
AI is a controversial topic in the context of cybersecurity because it can be used both by companies to defend against cybercriminals and by cybercriminals to attack companies.
Data poisoning refers to a cyberattack where cybercriminals alter AI model integrity by injecting minimally-perturbed samples into the training datasets. The wrongdoers can subtly manipulate datasets for AI algorithms training using their own AI models and gradually change AI systems’ outputs the way they want without raising suspicion.
Those popup windows that ask you to use more complex passwords are more than just an annoyance. Modern deep-learning-based password guessing systems can easily link many data traces that you have left on the limitless expanse of the internet to your personality and guess a simple password in a matter of seconds. People that use their favorite color or model of car, dog’s name, date of birth, or beloved song name for passwords stand no chance against today’s AI-based systems. This is why it’s highly advisable to use long passwords that make absolutely no sense to thwart such AI-powered attacks.
Most flashy uses of AI among hackers revolve around deepfake manipulation. It's now possible to impose a new deepfake identity onto a participant in real time, thanks to DeepFaceLive, a streaming implementation of DeepFaceLab, the most popular open-source project for superimposing photorealistic identities onto individuals in video footage. The most famous deepfake crime to date was defrauding $243,000 from a company CEO, with an audio deepfake impersonation of a chief executive used to obtain a wire transfer. Now that it's possible to add video to audio deepfakes in real time, the attacks can be highly realistic.
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