Artificial intelligence (AI) is a field of computer science that is being increasingly adopted in cybersecurity to combat cybercriminals’ ever-evolving tactics, techniques, and procedures (TTPs). AI revolutionizes the way we approach security by analyzing large amounts of data in real-time, enabling more effective threat detection and faster response times at scale.
However, as the term gains popularity, it’s often used as a marketing buzzword. It’s often difficult to understand exactly what AI is and isn’t, as well as how it’s most effectively used in cybersecurity. This blog breaks down what AI means in cybersecurity, its future, and how it can also spell security concerns moving forward.
What is AI?
AI refers to the development of computer systems that can perform tasks that would normally require human intelligence, such as perception, reasoning, learning, and decision-making. AI technology encompasses a range of techniques, including machine learning (ML), natural language processing, robotics, and computer vision, among others.
It’s accomplished by feeding computer systems large data sets, which they use to develop and understand correlations and connections. Then, in the future, the AI model can understand other data that it ingests under the same pretense.
AI vs ML
AI is often lumped together with other buzzwords, particularly ML. ML is a subset of AI that focuses on the development of algorithms and models that enable computers to learn from data and make predictions or decisions.
AI is a broader field encompassing various techniques to create intelligent systems, while ML is a specific approach within AI that focuses on training models with data to make predictions or decisions. ML is a tool used in the development of AI systems, but AI can also include non-ML approaches.
How is AI used in cybersecurity today?
AI can be used to detect and prevent attacks, as well as to analyze data and identify patterns that could indicate an attack is imminent. Here are some of the most important use cases of AI in cybersecurity:
- Threat detection and prevention: AI, specifically ML, algorithms can be trained to identify anomalous patterns in network traffic, email messages, and other digital communications that may indicate an attack is underway. By detecting these patterns early, AI systems can alert security teams to the threat and provide recommendations for how to prevent the attack from succeeding.
- Malware analysis: AI can be used to analyze malware and identify its characteristics, such as its static attributes, code structure, and behavior. This information can be used to develop new tools and strategies for detecting and preventing malware attacks.
- Vulnerability analysis: Cybersecurity experts often conduct vulnerability analysis to identify weaknesses in digital systems that could be exploited by attackers. Vulnerability assessments require getting versions of software, and once the versions are known, experts can compare those against known vulnerable versions. AI helps conduct vulnerability analysis by automating scanning and detection processes, rapidly identifying potential vulnerabilities, and analyzing patterns and anomalies in large datasets, enabling proactive mitigation strategies and prioritization of critical vulnerabilities.
- Incident response: When an attack occurs, it is important to respond quickly to contain the damage and prevent further harm. AI can be used to automate parts of the incident response process, such as analyzing network traffic and identifying compromised systems by identifying anomalous patterns. This can help security teams respond more quickly to attacks and limit their impact.
- User behavior analysis: AI can be used to analyze user behavior and identify patterns that could indicate a security threat. For example, if a user suddenly begins accessing sensitive data or logging in from an unusual location, an AI system could flag this behavior as suspicious and alert security teams to investigate further.
How is AI changing the future of cybersecurity?
AI is changing the future of cybersecurity in many ways, with the potential to improve detection, response, and overall security. Some of the ways that AI is changing the future of cybersecurity include:
- Automating tasks: AI can automate many routine cybersecurity tasks, freeing up security teams to focus on more complex issues. This can improve efficiency and reduce the risk of human error. We’ve already seen this with innovative new tools such as ChatGPT which is already helping to improve processes and streamline organizations’ day-to-day tasks.
- Adapting to new threats: AI can be trained to learn and adapt to new threats as they emerge, allowing organizations to respond more quickly to changing cybersecurity risks.
- Improved user authentication and fraud detection: AI can be used to improve user authentication by analyzing behavioral patterns and other data to identify potential fraud or malicious activity.
- Intelligent threat response: AI can be used to develop intelligent threat response systems that can detect, contain, and respond to attacks in real-time. This can help organizations to mitigate the damage from an attack and prevent further damage from occurring.
Overall, AI has the potential to significantly improve the effectiveness of cybersecurity measures, allowing organizations to better protect their systems and data from cyber threats. As AI technology continues to develop, it is likely to play an increasingly important role in the future of cybersecurity.
Does AI pose cybersecurity concerns?
Although AI can be a valuable tool in cybersecurity, it can also be manipulated and used by threat actors with malicious intent, posing multiple potential security threats. Here are some ways that AI can be a threat to cybersecurity:
- Adversarial attacks: AI models are vulnerable to adversarial attacks, where attackers manipulate input data to trick the model into making incorrect predictions or decisions. Adversarial attacks can be used to bypass security measures or gain unauthorized access to systems or data.
- Data poisoning: AI relies on large amounts of data to learn and make decisions. If an attacker can manipulate or corrupt this data, they can introduce biases or errors into the AI model, leading to incorrect predictions or decisions.
- Model stealing: Attackers can steal AI models and use them to develop their own attacks or gain unauthorized access to systems or data. This can be done by reverse-engineering the model or stealing it through a data breach or other means.
- Privacy concerns: AI systems may collect and analyze large amounts of personal data, raising concerns about privacy and data protection. If this data falls into the wrong hands, it could be used for malicious purposes, such as identity theft or fraud.
- Misuse by insiders: AI systems may be misused by insiders who have access to the system or data. This could include using the system to gain unauthorized access or steal sensitive data or manipulating the system to carry out unauthorized actions.
Learn more
Cybersecurity is an industry that is no stranger to buzzwords, AI included. AI has the potential to transform many different aspects of our lives and businesses, but it’s important to understand exactly what it is and isn’t, especially in terms of protecting your data.
ML is another commonly overused term in the industry. To better understand its role in cybersecurity, check out our blog.
