How Artificial Intelligence (AI) Can Resolve The Cybersecurity Issues
By Kamal Jacob
Since its original conception in the early 1950s, Artificial Intelligence (commonly referred to as AI) has witnessed an exponential rise in various industry domains including healthcare, manufacturing, and education. Adobe reports that in 2018, just 15% of the enterprises have adopted AI in their product strategies, however another 31% are on course towards using AI in the next 1 year. A 2017 survey conducted by BCG and MIT Sloan Management reveals that 70% of the surveyed executives expect AI to play a significant role in their companies over the next 5 years.
Such is the level of advanced sophistication offered by AI and machine learning (ML) that both these technologies are also been effectively utilized by hackers and cybercriminals to launch highly efficient and successful malware and botnet attacks across the globe.
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Some of the recent successful cyberattacks and data breaches that have been enabled by AI include the:
1. The April 2018 data breach of TaskRabbit, the online labour marketplace that reportedly affected over 3.75 million users.
2. The recent declaration by WordPress security about the AI-powered Botnet attack that compromised over 20,000 WordPress websites.
3. The November 2018 data breach of luxury hotel brand, Marriott that affected around 500 million customers.
All these statistics definitely suggest that cybersecurity, in its current form, is not working even though global organizations continue to spend close to $100 billion in a variety of cybersecurity products. Along with the increasing number of data breaches, the economic cost of cyberattacks are also growing at an alarming rate.
Security professionals are now pondering over the prospect of using AI and ML in improving cybersecurity practices. While technologies like AI and ML are proving to be a threat for cybersecurity, they can also be the solution.
In the following sections, we shall discuss the role of AI and ML technologies in cybersecurity and how it can resolve some of the common challenges in cybersecurity.
Why Cybersecurity Needs Artificial Intelligence
Deep learning technologies like ML and Artificial Intelligence have been hugely successful in problem domains like detecting objects in images and videos, natural language processing, self-driving vehicles, and product recommendations. Along with generating large data volumes, these domains are ideal for deep learning algorithms as they follow well-defined rules and have no adversaries involved.
So the question is can deep learning technologies be effective in cybersecurity which has:
a) Complex adversaries
b) No rules to follow
c) No labelled data on malicious malware code
The answer is Yes! The increasing complexity and sophistication of both AI and ML technologies makes them ideal for achieving cybersecurity and ensuring a safe and secure IT environment.
Here’s a look at 3 common challenges faced in cybersecurity and how AI/ ML can help in resolving them.
Challenge 1: Faster Detection of Cyberattacks
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AI-powered cybersecurity solutions are being developed for various categories including:
1. Fraud detection
2. Network intrusion
3. Early detection of malware and botnets
4. Spam applications
5. Hacking forecasts
6. And so on…
AI-based solutions can be configured to detect any software tool irrespective of whether it is a regular software tool with AI features or an AI-powered malware attack. Using machine learning technology, Cybersecurity firm, Darktrace claim to have detected 63,500 new or unknown online threats across 5,000 networks.
AI-based academic research is being used to detect cyberattacks with a success rate ranging between 85-99%. What are some of the parameters used by AI solutions to analyse the threat levels of any software tool?
1. Processor power and bandwidth consumed
2. Level of transmitted data
3. APIs and fields that are being accessed
Through valuable insights and analysis, AI technologies including machine learning can identify online threats such as malicious files or requests from suspicious IP addresses in a matter of minutes thus enabling faster response to these threats.
Challenge 2: Protection from Cyberattacks
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Thanks to constant innovation and advancement, AI has progressed from being mere providers of technical assistance to cybersecurity professionals to a more proactive role in detecting and preventing a variety of cyberattacks. With an increasing number of devices being connected on the Internet, the industry is facing an increasing shortage of skilled cybersecurity workers. This is boosting the industry’s increasing adoption of deep learning technologies for preventing cyberattacks.
The Cybersecurity domain is benefitting from AI-enabled protection systems that are using AI and ML to improve defences and prevent attacks. By combining supervised and unsupervised learning, AI in cyber-defence is being deployed to predict new online threats based on the current patterns.
The growing vulnerability of password-based protection in login pages is prompting the use of AI in developing biometric login methods using fingerprint and retinal scans, which are more secure than traditional user credential-based systems.
Another area of concern in cybersecurity is the increasing number of data breaches resulting in loss of valuable customer data. Data Science can play a critical role by designing and building security protocols and algorithms that can detect the data chunks that are most frequently targeted by hackers. Using a mix of data encryption and complex signatures, you can prevent unauthorized probes into this dataset.
Challenge 3: Conditional Access to Security Networks
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With the increase in the number of remote workers including business leaders accessing your business data from remote locations, organizations need more efficient authentication models that can secure its data from unwanted personnel. A “traditional” authentication model may prove to less effective in providing conditional access across geographical locations and networks.
On the other hand, an AI-enabled global authentication framework is proving more effective in assigning and altering access rights (or privileges) on the basis of user profile and location. Using Multi-factor authentication, AI systems can collect and analyse user information on the basis of user behaviour, intended application, device, and their location. With this user information, the AI-enabled system can assign or change the user privileges thus ensuring a higher degree of data security.
Why Cybersecurity Is Also Important for AI-Enabled Products
Thanks to the capabilities of deep learning technologies like AI and ML, companies are investing in these technologies for a variety of products and services. According to the latest industry predictions for the year 2020, a majority of both software and mobile app developers plan to use AI in their products. This includes the use of AI and ML technologies for faster and accurate credit scoring by banks, improvements in medical diagnosis systems, and enhanced manufacturing capabilities. Along with these capabilities, AI-enabled products also pose a higher security risk as an AI-automated system can be compromised and go undetected for an extended period as there is no day-to-day manual intervention by any human personnel.
Here’s the good news. Companies can tap into AI and ML capabilities to ensure cybersecurity at the product or application level. Deep learning technologies are strengthening cybersecurity in popular applications and security systems. Among the simplest examples, the Gmail tool is using machine learning to filter emails and protect the user account from unsolicited or malicious emails. Cybersecurity solution provider, Balbix is deploying AI-enabled risk predictions to protect against security and data breaches.
With the rising numbers and growing complexity of cyberattacks across the globe, traditional modes of cybersecurity are proving to be inadequate for countering these threats. The growing number of AI- and ML-enabled products offer advanced technological capabilities but also pose a security threat that can be exploited by cybercriminals.
Through this detailed article, we have highlighted how artificial intelligence can resolve some of the common challenges related to cybersecurity and why more companies are investing into AI and ML for addressing their security needs.
Let us know what you think about the role of AI technology in cybersecurity. If you are interested in exploring a career in cybersecurity, be sure to check our cybersecurity training course on our platform.