How is AI being used in cybersecurity?

AI security: Key takeaways

• AI transforms cybersecurity by automating threat detection, reducing response times, and providing real-time visibility into risks.

• AI-based cybersecurity tools (e.g., anomaly detection platforms, AI-driven threat intelligence) can identify and neutralise attacks more quickly than traditional systems.

• AI-powered security solutions add scale and accuracy, helping security teams cut through noise, reduce false positives, and prioritize real threats.

• Integrating AI with cybersecurity requires planning: start with clear use cases (like threat detection), ensure clean and secure data, integrate AI with existing systems (SIEM, firewalls, EDR), and continuously monitor models.

• Use cases include malware detection, vulnerability management, fraud detection, and insider threat prevention—where AI augments human analysts.

• Challenges remain, such as data bias, adversarial attacks, and the need for explainability in AI models.

• Best practice: Treat AI as an augmentation to security teams, not a full replacement. Human oversight is essential to manage risk.

Integrating AI in cybersecurity

AI is revolutionizing the field of cybersecurity with advanced tooling and techniques to combat evolving threats. AI-driven cybersecurity solutions provide enhanced threat detection, automated incident response, and fortified defenses against malicious activities. 

Integrating AI with cybersecurity enhances an organization’s defense posture—but requires strategic planning.

• Start with high-impact use cases like threat detection or anomaly spotting, where AI adds clear value.

• Ensure data quality and security: Clean, privacy-compliant data improves model accuracy and trust.

Layer AI on existing systems: Integrate with SIEMs, firewalls, and EDR platforms to maintain workflow continuity.
Monitor models and adjust: AI tools evolve—so track performance, retrain models, and manage drift.

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.

Key capabilities of AI-powered 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:

• 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.

Why choose an AI-powered security solution?

An AI-powered security solution elevates enterprise defenses by combining real-time intelligence with automation.

• Proactive visibility: AI monitors network and endpoint activity to expose hidden threats faster than traditional tools.

• Scalable operations: AI reduces analyst workload, freeing your team to focus on high-level threats.

• Contextualized insights: Leveraging behavioral analytics, these solutions reduce false positives and improve accuracy 

Challenges of AI in cybersecurity

Some of the common challenges in AI code generation 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.

How Snyk integrates AI with cybersecurity

Snyk AI Trust Platform 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.

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!