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How is AI being used in cybersecurity?
How the emergence of AI is affecting cybersecurity
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AI is revolutionizing the field of cybersecurity with advanced tooling and techniques to combat evolving threats. With the power of AI, cybersecurity solutions provide enhanced threat detection, automated incident response, and fortified defenses against malicious activities.
AI-based algorithms and machine learning models analyze vast amounts of data in real time, enabling proactive identification of vulnerabilities and swift mitigation of potential risks. AI's ability to continuously adapt and learn means AI is fast becoming an indispensable ally in safeguarding sensitive information and preserving digital security.
Keep reading to discover:
Four types of AI
In this article, we'll cover four commonly-used types of AI — generative AI, narrow AI, symbolic AI, and hybrid AI — and look at their main characteristics and applications.
Generative AI | This AI can generate original and creative content — such as images, music, or text — based on patterns and examples it has learned from. |
Narrow AI | This refers to AI systems designed for a narrow range of tasks or a specific domain. These systems are specialized and excel in a specific area, like speech and facial recognition or image classification. |
Symbolic AI | This rule-based AI uses logical reasoning and manipulation of symbols to simulate human intelligence. It explicitly represents knowledge and information using symbols, rules, and logic. |
Hybrid AI | This refers to a combination of multiple AI models, techniques, or approaches. It leverages the strengths of different AI paradigms — such as machine learning, symbolic reasoning, or expert systems — to effectively solve complex problems. |
Own AI security with Snyk
Explore how Snyk’s helps secure your development teams’ AI-generated code while giving security teams complete visibility and controls.
Six use cases for AI in cybersecurity
Harnessing AI's capabilities to enhance threat detection, automate incident response, and fortify defenses against malicious actors makes the power of AI suitable for various cybersecurity use cases, like the ones listed below:
Secure code generation: Generate secure and reliable code for software applications automatically with AI. AI techniques can analyze coding patterns, identify vulnerabilities, and generate code that adheres to security best practices, reducing the risk of potential exploits.
Threat detection: Employ AI to detect and identify potential cybersecurity threats in real time. Machine learning algorithms analyze vast amounts of data — including network traffic, logs, and user behavior — to identify patterns indicative of malicious activities or abnormal behavior, enabling prompt detection and response.
Security rules and policies management: Utilize AI to create and manage security policies and procedures within a cybersecurity framework. By analyzing historical data and adapting to evolving threats, AI can suggest improvements to security policies, optimize rule sets, and recommend actions to enhance overall security posture.
Incident response: Use AI algorithms to quickly analyze and prioritize incoming security incidents, provide real-time alerts, and even automate remediation actions — minimizing response time and mitigating the impact of security breaches.
Threat intelligence: Employ AI to gather, analyze, and interpret large volumes of data from diverse sources to provide actionable threat intelligence — including identifying emerging threats, analyzing threat actors' tactics, techniques, and procedures (TTPs), and predicting future cyber attacks.
Authorization and access control: Utilize AI to enhance authorization and access control mechanisms. By analyzing user behavior, context, and historical data, AI can detect anomalies or suspicious activities, helping to prevent unauthorized access, detect insider threats, and strengthen overall access control mechanisms.
Three challenges of AI in cybersecurity
Developers only accept around 20% of AI suggestions when coding, indicating room for improvement in AI code generation. Some of the common challenges include:
Secure coding practices: AI tools (think: Chat-GPT or Copilot) speed up development, but they need to be paired with comprehensive security tools to guarantee that the code is secure.
Skill loss: Over-reliance on AI could lead to a decrease in workforce capabilities and expertise in general. An overreliance on generative AI can also create skill loss among developers and engineers.
Licensing: There are ongoing discussions and regulations addressing the use of AI-generated code, particularly concerning the usability of the code if (or when) AI copies unlicensed open source code.
Ethical considerations for AI in cybersecurity
Incorporating AI into cybersecurity raises several ethical concerns that require examination and, in some cases, mitigation.
Privacy concerns: The access AI systems have to sensitive data highlights the need for safeguarding user information with robust security measures, stringent data protection protocols, and privacy-enhancing technologies.
Algorithmic bias: AI systems can unintentionally perpetuate biases or discriminatory outcomes, requiring careful design and monitoring to ensure fairness and equity.
Keeping humans in the loop: While AI technology offers efficiency and automation, human involvement is essential for effective decision-making, accountability, and addressing complex and evolving threats. Human expertise ensures critical thinking, contextual understanding, and the ability to adapt strategies based on situational nuances.
AI hacking and manipulation: Manipulation of AI models, like data poisoning, highlights the evolving threats of malicious actors trying to exploit AI systems. These bad actors continuously discover novel techniques to manipulate AI algorithms, compromising AI-based systems' integrity, reliability, and security. Data poisoning involves injecting malicious or deceptive data into training, leading the AI model to make incorrect or biased predictions. This emerging form of attack underscores the need for robust defenses and proactive measures to identify, prevent, and mitigate AI attacks in cybersecurity.
The personification of LLMs: People often perceive human-like behavior from large language models (LLMs). However, it is crucial to understand that this is a consequence of an LLMs' extensive training. LLMs lack the comprehensive understanding and cognitive abilities of the human brain. Users need to be mindful that AI can still be deceived and manipulated. Although it can simulate aspects of critical thinking, it is ultimately an artificial system and not equivalent to genuine human reasoning. Being aware of these limitations is essential when engaging with AI-powered technologies.
Future trends: AI + cybersecurity
Organizations and developers will continue to explore and integrate AI into cybersecurity tools and workflows, with several exciting prospects on the horizon.
The first thing that excites us is hybrid AI — combining different AI technologies to overcome the limitations of LLMs. Here at Snyk, we employ hybrid, symbolic AI to verify the LLM responses of our auto-fix suggestions in SnykCode. This approach leverages multiple models to provide effective remediation to developers, and addresses the downsides of trusting generated code from a single LLM model as mentioned above.
We are also seriously excited about integrating AI-specific processors, such as neural networks, into new devices that will enable the deployment of more robust AI models on increasingly compact devices — for example, mobile devices accessing and benefitting from ChatGPT.
Another significant evolution that we eagerly anticipate is democratizing AI models. With an ever-growing number of open-source language models available, developers can train their models for specific use cases, promoting greater accessibility and customization in AI-powered cybersecurity solutions.
These, and many other, developments hold promise for advancing AI integration in cybersecurity, fostering efficiency, accessibility, and improved model optimization. Don't forget to check out the full list of AI-related terminology for developers and security teams.
How Snyk integrates AI with cybersecurity
Snyk integrates AI with cybersecurity by leveraging machine learning to analyze code and detect vulnerabilities, allowing organizations to identify and address potential security risks.
With the Snyk platform, you have robust cybersecurity functionalities like:
Deepcode AI engine: Find and fix vulnerabilities and manage tech debt. DeepCode AI powers Snyk's one-click security fixes and comprehensive app coverage, allowing developers to build fast while staying secure.
AI code security fixes: Fix complex code security issues in IDEs, while protecting code ownership and integrity
AI-assisted security rules: Review all the security rules used by Snyk Code when scanning your source code for vulnerabilities.
AI code checker: Check your code security before the next PR commit and send alerts of critical bugs. (You can try the free online code checker, powered by Snyk Code, now!)
Snyk Open Source: Scan and monitor open source components, providing vulnerability detection and remediation guidance.
Snyk Advisor: Help find the best packages for open source projects.
Interested in learning more about how we harness the power of AI to enhance our cybersecurity posture? Book a live demo with a security expert today!
Own AI security with Snyk
Explore how Snyk’s helps secure your development teams’ AI-generated code while giving security teams complete visibility and controls.
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