Lately, the technology sector seems to be centered around one key topic: Artificial Intelligence.
And for good reason! AI is an immensely powerful tool that continues to expand in both its application and reach. However, there is still considerable confusion surrounding various terms related to AI and security. In this blog, we’ll explore the differences between securing AI, secure AI use, AI for security, and AI safety.
While these concepts are related and can even complement each other, each has a distinct focus.
Securing AI
Securing AI refers to protecting the AI system from attacks or misuse, a concept that most people (or at least cybersecurity professionals) immediately think of. This involves safeguarding the AI from threats and ensuring its models stay secure. Attackers may attempt to steal the AI model or the data it processes, or they may aim to subvert the AI for their gain.
Key risks in this area include model theft, privacy violations, adversarial attacks, and data poisoning.
Secure AI Use
Secure AI use aims to prevent or mitigate harm resulting from the misuse or malfunctioning of AI systems.
Shadow AI
Though “shadow AI” may sound like a fictional villain, it’s a very real and mundane risk for organizations. Shadow AI refers to AI usage that is hidden from or not visible to an organization’s IT and security teams.
Despite best efforts, many people turn to AI for help in their work. In many cases, this is harmless (as long as the results are verified). However, in certain sectors, employees may unknowingly input sensitive company information into personal AI accounts, which can then be exposed or shared with unauthorized users. Such breaches can result in substantial financial losses and damage to public trust.
AI Safety
AI safety involves designing AI to operate ethically and avoid causing harm to individuals or society. This concept is slightly different from the others, as it’s concerned with ethics and compliance rather than strict security measures. However, since these topics are often misunderstood, we’ll include it here.
Creating an AI model entails setting parameters for what the AI can and cannot share with humans. This requires collaboration between ethicists, policymakers, and AI researchers to establish clear guidelines for AI behavior. Ensuring AI doesn’t deliver biased responses is also a critical part of AI safety.
Insecure AI can pose safety risks. For example, in the early stages of LLMs, users were able to manipulate ChatGPT into disclosing harmful information by framing requests in specific ways. While the model was designed to refuse requests like “How to make a bomb,” it could be tricked into providing harmful instructions if the prompt was reworded creatively. Failure to close such “loopholes” can result in serious legal consequences and even harm to people in the real world. Although this is not the primary focus of cybersecurity, it remains an important issue.
AI for Security
AI for security (also known as AI security) refers to using AI to enhance cybersecurity measures. Due to its ability to analyze code and natural language, AI is an ideal tool for various cybersecurity applications. For instance, AI can detect incoming threats like malware or monitor emails for phishing attempts, automatically flagging suspicious messages.
There are countless ways AI can be applied to strengthen cybersecurity, but it’s essential to remember that malicious actors can also use AI for attacks. The battle between cybersecurity professionals and cybercriminals is ongoing.
| Securing AI | Secure AI Use | AI for Security | |
| Primary Goal | Protect AI systems and models from threats | Manage risks of unauthorized AI tools | Use AI to improve cybersecurity |
| Focus | AI as the asset being protected | Unregulated AI usage within an organization | AI as a tool for defense and automation |
| Key Stakeholders | AI developers, data scientists, cybersecurity teams | IT security teams, compliance officers | Security analysts, IT professionals |
| Threats Addressed | Adversarial attacks, model theft, data poisoning | Data breaches, regulatory violations | Malware, phishing, insider threats, etc. |
| Example | Hardening an AI model against adversarial inputs | Restricting unauthorized AI usage | AI detecting anomalies in network traffic |
Conclusion
To summarize, securing AI ensures the model remains trustworthy, protecting it from being poisoned or stolen. Secure AI use focuses on preventing the leakage of private data to unauthorized users. AI for security, on the other hand, is about leveraging AI to counter external threats to applications and systems.
