AI could be digging into your dirty laundry, so to speak.
All businesses have their secret formula for staying competitive and differentiating themselves from their competitors. If you don’t implement your AI strategy correctly, you risk exposing all the sensitive data from your company, potentially leaking your methods and what makes you unique.
While leading AI companies claim they let you opt out of using your content to train their GenAI algorithms, it can be challenging to track what happens with the data your employees input into LLMs.
Given that these companies have overwhelmingly long, complex, and frequently changing privacy policies, it is difficult to know if the steps we are taking are truly safe for our business.
But the solution is not to avoid using AI at all.
On the contrary, this approach will only cause you to lag behind your competitors and fail to meet your customers’ demands for better services.
In this guide, we’re here to help you adopt AI in your business in a trusted and secure way, with the right strategy.
I. Make Sure What Happens with the Data Inputted into the LLMs
Companies like OpenAI and Google claim they do not save user data and offer opt-out options for those who don’t want their conversations used to train AI models. But is that enough?
Big tech companies have a history of using private data for unintended purposes, and their terms are often lengthy, filled with technical jargon, and frequently exposed to changes.
This can make it unclear what happens with the data. Is the data used to train LLMs and could it further appear in the outcomes? Do AI companies have access to the information that an employee inputs? How secure is the data during transmission and storage? Are there any third parties involved in data processing?
All these are the questions you have to think about before your company fully embraces AI.
II. Make Sure the Model Doesn’t Have Potential Vulnerabilities
When integrating AI into your business, it’s crucial to ensure that the model doesn’t have potential vulnerabilities that could be exploited. Here are some of the main vulnerabilities to watch out for:
Phishing
This threat involves attackers deceiving employees into providing sensitive information, such as login credentials or financial information, by masquerading as a trustworthy entity. In the context of AI, sophisticated phishing attacks can involve the use of deepfake technology to create convincing audio or video messages from executives, tricking employees into revealing confidential data.
The head of WPP was recently targeted by an elaborate deepfake scam. Fraudsters used a deepfake audio clone of CEO Mark Read to impersonate him in a Microsoft Teams meeting, attempting to solicit money and personal details. The attackers created a WhatsApp account with a publicly available image of Read and set up the meeting using this image and a voice clone. Although the scam was ultimately unsuccessful, it highlighted the increasing sophistication of cyber-attacks on senior executives.
Prompt Injection
Prompt Injection is a technique where an attacker inputs crafted prompts into an AI system to manipulate its outputs. This can lead to the AI generating harmful or misleading information.
For example, a malicious user inputs deceptive prompts into an AI chatbot, causing it to provide inaccurate or harmful responses.
Data Poisoning
Data Poisoning occurs when attackers manipulate the training data to corrupt the model. By injecting false or misleading data into the training set, attackers can influence the model’s behavior and outputs in a detrimental way.
The tool Nightshade, developed by researchers at the University of Chicago, allows artists to “poison” datasets used to train generative AI models. Although Nightshade is designed to protect artists’ work from unauthorized use, it serves as a practical example of how data poisoning works. The tool subtly alters pixels in a way that is imperceptible to human eyes but disrupts the training process of AI models, leading to the generation of distorted or low-quality outputs.
Model Inversion
Model Inversion involves attackers using outputs from an AI model to infer sensitive details about the training data. This can lead to the exposure of confidential information.
For example, an attacker uses model inversion techniques to reconstruct private data, such as personal health records, from the outputs of a machine learning model.
III. Make Sure the Model Isn’t Using Copyrighted Content
Is your AI-generated content infringing on copyright laws? This question is really relevant today, and more and more authors and news sites are suing AI companies for using copyrighted material like books, paintings, and paywalled articles to train their language models. For instance, artists have taken legal action against AI firms for using their artwork without permission. Now, these companies are scrambling to remove copyrighted content from their datasets.
So, how does this affect you? Imagine launching a marketing campaign with AI-generated images or text, only to find out they include copyrighted material. This scenario could lead to costly legal disputes and several financial penalties.
To avoid such pitfalls, exercise caution with AI-generated content. Ensure that the AI tools you use are compliant with copyright laws to prevent any legal entanglements.
What Can You Do to Mitigate Risks?
You are aware of the dangers. Now, it’s time to follow a strategic path to start using AI in a secure and trusted way.
1. Bring Your Own Model (BYO)
Developing a custom AI model tailored specifically to your company’s needs and data sensitivity levels provides greater control over data usage and security. This approach allows you to oversee the entire training process, ensuring that the data used is clean and devoid of potential vulnerabilities. By owning the model, you can implement robust security protocols, such as encryption and access controls, to protect sensitive information.
2. Evaluate AI Providers and Technologies
When choosing third-party AI solutions, conduct thorough evaluations of potential providers. Assess their security measures, data handling practices, and compliance with industry standards. Look for providers who offer transparency in their operations and have strong privacy policies in place. Additionally, ensure they provide options for data anonymization and opt-out mechanisms to prevent unauthorized use of your data.
3. Be Aware of the Human Factor
It’s fair to say that human error is the biggest threat to data breaches, accounting for over 80% of incidents.
Before using any large language model like ChatGPT, Gemini, Jasper, Claude, or any other, it’s crucial to mitigate this risk. Implement comprehensive training programs to educate employees about best practices, such as recognizing phishing attempts and securing sensitive information. Establish clear protocols for handling data and ensure that employees understand the importance of adhering to these guidelines. This proactive approach will help reduce the likelihood of human error leading to a data breach.
4. Understand That Threats Evolve
Threats evolve, and so should your defenses. Attack vectors such as adversarial attacks, data poisoning, and model inversion are becoming more sophisticated.
To stay ahead of these threats, businesses must continually adapt and learn. This includes staying updated with changes in LLM policies, such as those related to data usage and privacy, and monitoring for new bugs and vulnerabilities detected in AI systems.
5. Get Strategy Experts
You might not have the expertise or the skills necessary to craft a perfect AI roadmap, but don’t worry—you can always look for external help.
If you’re unsure where to start and want to avoid missteps, look for an implementation partner who can tailor a specific route for your business’s unique needs, leveraging AI to its maximum potential while mitigating associated risks.
Your partner can help ensure you have everything you need to go ahead confidently. Consider asking these questions:
- Do I have the specific roles covered?
- Do I have the in-house knowledge to train people?
- Do I know which are the best models for our needs?
- What type of data will be used?
- How is the data being protected?
- How are we monitoring the AI system and data usage?
By addressing these questions with a partner who has extensive expertise, you can ensure you’re on the right track without worrying about missing critical steps.
Conclusion
Don’t rush into AI without first ensuring that your business’s reputation is safe. Develop a solid AI strategy, ensure you have the right talent on board, and then start transforming your business into an AI-driven powerhouse.
At Inclusion Cloud, we understand the complexities and challenges of integrating all your company’s data into AI. We offer tailored solutions and expert guidance to help you navigate these waters safely. Our team of specialists, including data scientists, machine learning engineers, and architects, will work with you every step of the way to build a robust AI strategy that protects your data and ensures compliance with all relevant regulations.
Contact us today to learn how we can help you! If you enjoyed this article, follow us on LinkedIn for more similar content.