Artificial intelligence in cyber security has become a strategic priority for organizations that want to leverage the potential of AI without exposing themselves to critical risks. The adoption of artificial intelligence in the enterprise is growing rapidly, bringing with it extraordinary opportunities but also new security challenges.
Why Artificial intelligence in cyber security Is Different from Traditional Cybersecurity
Securing AI systems doesn’t simply mean applying classic cybersecurity measures. AI models, particularly those based on machine learning and deep learning, introduce new attack surfaces. Among the emerging threats:
- Data poisoning: Manipulation of training data to induce incorrect behaviors in the model.
- Model inversion: Attacks that attempt to reconstruct sensitive data from model outputs.
- Prompt injection: A technique where a malicious actor alters the input provided to the model to manipulate its behavior.
These risks require specific approaches and new tools in the field of AI cybersecurity.
Governance and Responsibility: A Crucial Issue
One of the key points raised during the webinar concerns the lack of structured governance for the use of AI within companies. Often, AI tools are adopted spontaneously by individual departments without centralized oversight. This increases the attack surface and makes it difficult to implement effective controls.
To address these challenges, it’s essential to:
- Establish a C-level AI governance strategy.
- Map the AI tools already in use across departments.
- Define company-wide policies to regulate AI usage from a security perspective.
AI and Shadow IT: A Growing Problem
The proliferation of AI tools used without IT approval—so-called shadow AI—is becoming increasingly relevant. Business users, often drawn to the productivity boost AI can offer, use uncontrolled tools that may expose the company to data leaks, vulnerabilities, and regulatory violations.
Companies must therefore:
- Raise user awareness about the risks of unauthorized AI use.
- Implement monitoring tools for AI usage within cybersecurity frameworks.
- Introduce authorization mechanisms and whitelists of approved tools.
AI and Data Protection: Artificial intelligence in cyber security
With the use of generative models and tools like chatbots, new data-related issues emerge. Prompts may contain confidential or critical company information. It is therefore essential to:
- Define guidelines for prompt writing.
- Implement AI-specific data loss prevention (DLP) systems.
- Apply automated redaction techniques to remove sensitive data.
Toward Cybersecurity by Design for AI
Artificial intelligence in cyber security must be integrated from the earliest stages of designing an AI-based system. This means:
- Involving the security team from the start of the AI project lifecycle.
- Evaluating models not just on performance, but also on robustness and explainability.
- Integrating AI-aware auditing and logging tools capable of tracking model outputs and decisions.
Conclusion
The widespread adoption of artificial intelligence in cyber security calls for a paradigm shift in cybersecurity. AI cybersecurity cannot be a mere adaptation of traditional security—it must be rethought with a targeted approach. Companies that face this challenge with awareness and strategy will not only reduce risks but also accelerate the safe adoption of AI.