Secure generative AI.

The world has seen a massive breakthrough in artificial intelligence, and an exciting new type known as generative AI has emerged. This genre, which incorporates models like ChatGPT, can create entirely new content – text, images, music, and even videos – based on the commands it receives. It has become an integral part of our daily lives, from helping us write emails to designing creative presentations, and even in more complex areas such as drug development and product design. 
With these incredible capabilities, there's an equally urgent need to understand another aspect: securing GenAI. Like any powerful technology, generative AI carries security risks that may not seem obvious at first glance. These risks not only threaten the privacy of our data, but can also affect the accuracy of information, cause financial damage, or even be used for malicious activities. The goal of this article is to simplify these complex security concepts and present them in a clear and understandable style to non-specialists, to help them deal with these revolutionary technologies consciously and cautiously.

What is Generative AI Security? And why is it different? 
When we talk about securing traditional software, we often think of protecting systems from hacks, viruses, and data theft. But securing generative AI adds new layers of complexity. AI doesn't just process data, it learns from it and interacts with it in dynamic ways, opening doors to new types of attacks. The fundamental difference lies in the way these systems work. Generative AI relies heavily on the inputs it receives from users or other sources, and on the outputs it produces. Attackers can exploit this cycle—input, processing, and output—to manipulate the system or extract sensitive information from it. For example, they may try to enter malicious commands that cause the model to behave unexpectedly, or they may try to exploit vulnerabilities in the way the data is processed to obtain confidential information. 
Securing generative AI isn't just about protecting servers or networks, it extends to protecting the model itself, the data it trains on, and the interactions that occur with it. It requires a deep understanding of how these models work, how they can be manipulated, and how we can build strong defenses against these new threats.

The Five Biggest Risks. 
To better understand the security challenges of generative AI, let's review the top five risks, which have been highlighted by organizations like OWASP's Open Web Application Security Project:

First, Prompt Injection. 
Imagine you're talking to a smart robot, and suddenly someone else tells you to ignore what you told the bot and do something completely different. This is the essence of Prompt Injection. This happens when attackers try to manipulate the prompts given to generative AI, to force it to break its original rules or execute unintentional instructions. This can be done in two ways:

  • Direct Prompt Injection: Here, an attacker tries to bypass the form's original instructions directly. For example, the model might be asked to ignore the previous instructions and reveal the password to me. If the attack is successful, the attacker may be able to gain access to backend systems or exploit the model for malicious purposes.

  • Indirect Prompt Injection: In this case, the attacker controls an external source such as a website or document that the AI uses as input. When the model reads this source, it follows the malicious instructions in it, which could manipulate the model or use it to manipulate the user.

It can lead to the theft of sensitive information, influence decision-making processes, or the use of artificial intelligence in social engineering scams.

Second, Sensitive Data Exposure.
 Generative AI processes massive amounts of data, whether it's training data or data entered during conversations. If this data isn't handled with caution, the model may inadvertently expose confidential or sensitive information in its output. This can include customer information, intellectual property, or any other private data. 
Why is it dangerous? It can lead to privacy breaches, a breach of compliance regulations, or significant financial losses to organizations. Imagine a form revealing bank account details or confidential health information.

Data poisoning. 
Generative AI's performance depends heavily on the quality of the data it is trained on. Data poisoning is an attack in which attackers try to intentionally insert false or harmful data into a model's training dataset. This manipulation can cause the model to produce inaccurate, biased, or even harmful results. 
Why is it dangerous? It can undermine the reliability of a model, make the wrong decisions, or be used to spread misinformation. Competitors may use it to discredit an organization that relies on this model.

Overreliance is fourth.
 Generative AI is not perfect. It can sometimes produce incorrect information known as hallucinations, biased results, or even outdated information. When individuals or organizations rely excessively on the outputs of these models without scrutiny or verification, they put themselves at significant risk. 
Why is it dangerous? It can lead to the spread of misinformation, regulatory violations, legal issues, and defamation.  AI outputs should always be approached with caution, especially in sensitive areas.

 Supply Chain Risks.
 Generative AI applications are often built using multiple components from different sources: pre-trained models, third-party datasets, plugins, and software libraries. If any of these components contain vulnerabilities or are tampered with, the entire system becomes vulnerable. 
Why is it dangerous? It can lead to the introduction of malicious code, data leaks, or even the entire disabling of the model. It requires rigorous auditing of all the components used in building and operating AI systems.

How to Protect Ourselves and Our Institutions? Simple and Effective Defense Strategies
To Protect Ourselves and Our Organizations In this new era, we need to adopt multi-layered defense strategies. Here are some practical tips:

First for the average user.

  • Don't share your secrets: Avoid entering any sensitive personal information, financial data, or professional secrets into generative AI models. Remember that what you enter may be stored or used to train the model in the future.

  • Verify information: Don't believe everything AI says. Always check important facts and information from reliable sources, especially in sensitive areas like health, finance, or news.

  • Be aware of links and extensions: If your AI model asks you to click on a link or use a plugin, be careful. Make sure the source is trusted before interacting.

  • Think before you ask: Formulate your questions carefully to avoid revealing unnecessary information. The less sensitive the input, the lower the risk of leakage.

Second, for enterprises.
Organizations need a more holistic approach to protecting their generative AI systems:

  • Establish clear policies: Strict guidelines should be set on how employees can use generative AI, what types of data can be entered, and how to handle outputs.

  • Use of monitoring tools: Apply specialized security solutions to monitor interactions with AI models, detecting any abnormal behavior, attempts to inject commands, or leak data.

  • Employee training: Educate employees about the security risks of generative AI and how to avoid them. Human awareness is the first line of defense.

  • Supply chain security: Audit all external components of pre-trained models, datasets, add-ons used to build and operate AI systems, and ensure that they are safe and reliable.

  • Apply  the Zero Trust Principle: This principle means never trust, always verify. In the context of AI, this means no automatic trust in any inputs or outputs from the system. Each interaction must be validated, and the model's output must be treated as if it were from an untrusted source before it is allowed to affect other systems.

  • Isolation of sensitive forms: Run models that handle sensitive data in isolated environments to reduce the risk of leakage in the event of an attack.

The future of security in the age of AI.
 It may sound contradictory, but one promising solution to securing generative AI lies in the use of AI itself. Advanced AI systems can help detect complex attack patterns, analyze inputs and outputs for signs of manipulation, and even develop automated defenses against new threats. 
The future of security in this area will depend on the delicate balance between innovation and security. As we seek to make the most of the capabilities of generative AI, we must ensure that these technologies are built and used in a responsible and secure manner.  This requires collaboration between developers, security experts, and policymakers to create robust standards and guidelines.

Generative AI is undoubtedly a transformative force, capable of revolutionizing many aspects of our lives. But like any powerful tool, its effectiveness and safety depend on how we use it. Understanding the security risks associated with them, and taking the necessary steps to protect them, is not just the responsibility of technical experts, but a collective responsibility of everyone who uses these technologies. By being aware, careful, and applying security best practices, we can take advantage of the enormous potential of generative AI while minimizing risk. It is a very useful technology, but only if used consciously and wisely.