In the 2024 IBM Cost of a Data Breach Report, 42% of companies cited AI and security automation as a major factor in their success in improving their cybersecurity posture. AI is changing the cybersecurity and technology sectors in many positive ways such as improving threat identification and response, but also in several negative ways such as increased complexity of cyber attacks or AI deepfakes. This blog will provide general education about AI and cybersecurity, explore how these technologies are impacting the security sector, and discuss examples of AI success stories.
The Basics of Cybersecurity
If you don’t come from a background in cybersecurity, you might get lost in all the malware, encryption, and firewall talk, so we figured we should start with the basics. Here are the 5 most common phrases and terms in cybersecurity that will help you understand this article, and the world of cybersecurity.
- Encryption: A method of protecting data by converting it into code that can only be accessed or decrypted back into data by someone who has the code.
- Firewall: A digital barrier that secures your network, acting like a security guard to ensure no harmful traffic is allowed into the network, and that no important data is leaked out of the network.
- Malware: Stands for “malicious software”. This term refers to any software designed by hackers such as viruses, to steal data, or damage/destroy computers, servers, networks, or data.
- Phishing: These are scams and deceptions created by hackers to manipulate people through social engineering to share private or sensitive information such as passwords or credit card numbers. These attempts often involve fake emails, texts, calls, etc. and can often be difficult to identify as scams.
- Deepfake: A new term created through the rise of AI in phishing scams, this term specifically refers to a video, audio file or image generated by AI to impersonate someone and make it appear as though they are saying something or doing something that they have never said or done.
The benefits of AI in Cybersecurity
Now that you are a cybersecurity expert, and since you’ve already read our Blog about AI Technologies, you already understand how machine learning (ML) can be trained to reduce the manual workload associated with many tasks. Just in case you missed it, machine learning and deep learning are subsectors of AI that are trained on large data sets and then learn from data to make decisions without explicit programming. These programs play a very important role in cybersecurity, as they reduce and eliminate many of the tasks that used to be executed by humans, reducing the time required for many tasks and eliminating the threat of human error. AI tools are used throughout the cybersecurity industry, to improve pre-launch testing, monitor and improve security for live systems, and handle threats and security breaches that have already happened.
In pre-launch testing, AI tools can be trained using data from previous attacks to mimic hackers to do vulnerability testing known as penetration testing, to identify vulnerabilities in a software system so that the team can address these concerns before the system goes live.
Once a system is live, AI tools are used broadly to monitor for threats, and these tools have been proven to significantly reduce the detection time for threats or anomalies. AI tools have the computational power to monitor and inspect a virtually infinite amount of data, and ML models can be trained to predict threats by continuously monitoring systems for suspicious network traffic and identifying problems before they become significant threats. An AI Cybersecurity company called Darktrace uses real-time self-learning algorithms to learn from its experiences to identify and isolate any cybersecurity threats, which takes cybersecurity from reactive to proactive. These tools use AI to make anti-malware, antivirus, and fraud detection software more potent.
If a security breach has already happened, AI tools (if integrated properly), have a detailed and deep understanding of security systems and can immediately identify how to handle the threats and contain the problem. The ability of AI and ML models to identify threats and ultimately make decisions about how to isolate these attacks to protect the systems is powerful in incident response.
Throughout these complicated cybersecurity processes, AI tools are also being integrated to reduce the manual workload on security teams. This allows these team members to focus their time on more high-priority tasks that require critical thinking skills.
The Challenges AI Poses in Cybersecurity
As great as AI is in improving proactive defence, monitoring and incident management in cybersecurity, the problem is that the “bad guys” have access to all of the same technology. Cybersecurity threats are evolving in complexity and sophistication as a result of AI tools being used in malware and viruses and by hackers around the world.
On a more personal scale, deepfakes are using AI to impersonate loved ones, colleagues, or public figures to execute scams and steal personal information, private data or company information. Deepfakes use a combination of AI technologies to replicate someone’s voice, generate images, or even generate a video where it appears that someone is saying something they have never said. This poses a great risk in cybersecurity as it means we can no longer trust everything we see, even if it is a video or phone call.
Individuals must be more diligent with using multi-factor authentication in their day-to-day lives. As much as we all hate multifactor authentication when we just want to log in to our email, there is a valuable lesson to be learned from this technology. As deepfakes can impersonate anyone, it is increasingly important to double-check any significant asks from your boss, family or friends, with another text, call, or email. If you get a text, voicemail or even video recording of someone you know asking you to send money, pay a contractor, or any other situation where they are asking you to share personal information, always authenticate this request with a callback, an email or a chat to ensure it is truly this person asking you.
Data Privacy in the Age of AI
For many of us, it has become common practice to use AI chatbots such as ChatGPT, Claude, Copilot or any of the other options in our day-to-day lives. What many people don’t know is that most of the free versions of these programs, and even some of the paid full versions use your data and responses to train their models. This poses a serious risk in terms of data security and privacy.
Before you ever share any personal information about yourself or your company, you need to be sure that you are using a model that does not store or train itself on your data. This information can be found in the privacy policies or settings. It is important to know if the data and responses you provide are being used by the bot or stored somewhere for future learning.
On the other hand, when the correct settings are applied, and the option to train and record personal data are turned off, most AI chatbots have very strong data security policies. They cannot share data with other sites, and they have strong data protection protocols to protect any stored information from cybersecurity attacks. For those using ChatGPT Enterprise or OpenAI APIs, data is protected to the highest security standard, known as SOC 2 compliance, and no data is used to train their models. If used correctly, there are extremely safe and secure ways to go about using these platforms, but users must educate themselves, and ensure they are using them correctly.
Real-World Applications and Success Stories of AI in Cybersecurity
There are countless examples of AI improving the world of data privacy and cybersecurity throughout the spaces and processes, from newer companies providing b2b services to improve cybersecurity for other networks, to large banks and service providers adopting AI tools to protect their client’s data.
IBM is a leader in the space of AI in cybersecurity, with AI tools integrated throughout their security processes. AI tools are used to detect suspicious or abnormal activity, improve incident response time, and automate threat response. The interaction of AI in these key areas ensures that the huge collection of data within their network is safe, and significantly reduces the losses due to fraud.
Balbix is an example of a more niche service provider specializing in AI-driven cybersecurity solutions on the b2b side. They are using AI to execute continuous deep monitoring and risk assessments of networks and their ML models learn from new data to improve accuracy in identifying vulnerabilities, attack vectors and potential threats.
Across the board, companies are taking advantage of AI’s computational power and ability to learn from itself and continuously improve to revolutionize the world of cybersecurity and data protection.
The Future of AI In Cybersecurity
As companies have increasingly more and more data, stored not only in their premise but also in cloud infrastructure, IoT (Internet of things) devices and in other applications, it is increasingly important for this data to be widely and thoroughly protected.
As AI tools improve within cybersecurity, AI incident remediation is one of the most promising innovations that will automate threat response rather than requiring human intervention. As the understanding of these AI tools grows deeper and more robust, they will gain the ability to accurately and efficiently act on detected threats, to cut out the human intervention altogether.
The sheer ability of AI to consume data has widespread implications in the cybersecurity world. As innovators push the boundaries of these tools, and as the general public warms up to the integration of these incredibly advanced technologies, AI will continue to augment and advance what we can do with data.
