How to Use AI Safely
There is no denying that AI is being used in every industry, with the sector's projected annual growth rate of at least 30% over the next ten years.
While most modern AI applications positively affect our lives, If artificial intelligence is misused, As AI becomes more sophisticated and widespread, so do its risks, including those posed by bias, privacy infringements, and other cybersecurity risks.
Let's discuss some common AI cybersecurity risks and how to protect yourself while using AI.
Choose AI applications wisely.
Not all AI applications are secure. To create fake apps that trick users into downloading them, exploit the growing demand for AI apps. If you install fake AI programs on your device, you'll have the best chance to install malware that will steal all of your data. It is crucial to conduct due diligence before downloading any AI app to reduce cybersecurity risks. Only using AI tools your company has tested and approved should be the rule of thumb.
Don't Use Personal Information
Cybercriminals are becoming more sophisticated and can seize any sensitive data or personal information stolen using your AI tools. In general, uploading sensitive information could end incriminating privacy laws, leading to fines and penalties. Never enter Personal Identification Information( PII) into AI chatbots like ChatGPT when conducting experiments or using AI tools.
Don't Rely on AI for accuracy.
Can bad data or malicious hackers spooky AI, as a standard query is? Yes, that is the straightforward answer. AI software produces inaccurate content. For AI to make accurate outputs, algorithms must contain large, representative data sets. If certain groups of data sets are underrepresented, it will ultimately result in inaccurate outcomes and harmful decisions. The AI tool you use is only as accurate as the data it uses. If the data it uses is old or incomplete, the content it churns out will be biased, inaccurate, or outright wrong.
For this reason, you should not rely on AI alone to make crucial business decisions. Always double-check the information an AI-powered device or service provides. Similarly, computer code generated with AI tools carries a similar risk. Recently, computer programmers have started using AI tools to write various codes. Although it may save them time, there is always an inherent risk of generating codes that carry various errors, increasing insecurity risks.
To minimize AI cybersecurity risks, use tools like Google Transparency Report Safe Browsing to crosscheck AI-generated content. Safe Browsing identifies unsafe websites and notifies users of potential harm. Check URLs you use against Google's regularly updated list of hazardous web resources.
Don't Use Plagiarized Content
It is entirely possible for an AI tool to match words or copy-paste words from other sources to create pieces of new content quickly. With many bloggers and businesses relying on AI writing programs for their websites, there is rising concern the work these tools produce could be plagiarized. When using AI tools to generate content for your website, you should be careful not to plagiarize content. Plagiarized content can result in penalties, including website exclusion from search engines such as Google. If you rely on AI to generate content, check for plagiarism using tools such as Turnitin and Copyscape.
Disable the Chat Saving Function
Turn off the chat saving function to reduce the risk of cybercrime and data breaches when using Chatbots such as ChatGPT. ChatGPT now allows users to turn off their chat history to prevent using their data to train and improve Openai's AI models. In the past, ChatGPT kept track of conversations and harvested related data to fine-tune its models. Although users were allowed to clear their chat history has needed, still, their conversation could still be used for fine-tuning. This posed significant privacy issues, especially where sensitive data is concerned.
Stay Alert for Any Suspicious Activity
AI cybersecurity risks are expected to increase rapidly as AI tools become cheaper and more accessible. While generative AI has multiple positive applications, there are also rising concerns about its potential misuse. For example, actors can use these tools to generate fake content or deepfake videos to deceive or manipulate people. In the realm of cybersecurity, generative AI can be used for malicious purposes, including generating convincing phishing emails and codes. Although apps like ChatGPT have some protections to prevent users from creating malicious codes, actors can leverage clever techniques to bypass them and create malware. Therefore, when using AI tools and content, you should always stay alert for any suspicious activity .
Discover practical strategies and gain valuable knowledge to protect your data from the advancements of AI technologies. By watching this webinar replay: Protecting Your Digital Frontier: Essential AI Cybersecurity Tips
What is the biggest issue with AI data protection and security?
Clear policies and guidelines for employees.
According to an Information Systems Audit and Control Association( ISACA) survey, only 10 percent of organizations have a formal, comprehensive, generative AI policy in place.
This article explores the top five strategies for your enterprise to protect and secure data when using AI and creating an AI company policy, emphasizing the importance of ethical guidelines, data masking, pseudonymization and transparency.
In an age where data is regarded as the new oil, concerns about privacy, especially in the realm of Artificial Intelligence( AI), have heightened. The intersection between data security and AI utilization has become critical with the rise of generative AI and Large Language Models( LLMs) like OpenAI's GPT series.
Here are six ways to maintain privacy while effectively harnessing the power of AI:
Differential Privacy:
Definition: Differential privacy is a system that ensures that the AI's output remains essentially the same whether or not a specific individual's data is included in its input. It introduces “noise “into the data, making it difficult to trace back to any source.
Application: When training models on large datasets, differential privacy ensures that no information about any individual data point can be inferred from the model's output. This guarantees that the individual's data remains private, even if the overall data trends are studied.
Homomorphic Encryption:
Definition:
Homomorphic encryption allows computation on ciphertexts, generating an encrypted result that, when decrypted, matches the result of the operations performed on the plaintexts.
Application:
For AI, this means models can be trained on encrypted data. The model never actually “sees” the real data, but can still learn from its patterns. Thus, sensitive data remains encrypted and secure throughout the entire AI training process.
Data Minimization:
Definition: Use only the data that's necessary. This principle originates from data protection guidelines and is increasingly relevant in AI.
Application:
In generative AI and LLMs, rather than using broad data, determine what specific data is necessary for the model's functionality and only utilize that. This limits exposure and reduces the risk of compromising extraneous data.
Federated Learning:
Definition:
Instead of sending data to a central server for training, the AI model is sent to where the data is stored( e. g., a user's device). The model learns locally and only sends model updates( not raw data) back to the server.
Application: This decentralizes the learning process. Especially in situations where data cannot be easily or legally moved, federated learning offers a way to train models effectively without compromising privacy.
Auditing and Transparency:
Definition: Regularly check the AI models and systems to ensure they adhere to privacy standards. Make the methodologies and practices public.
Application: Especially with LLMs that interact with vast amounts of data, having an audit trail ensures that any potential breaches or misuse can be tracked and rectified. Transparency builds trust and provides users with assurance regarding how their data is utilized.
Maintaining Data Security and Compliance:
Definition: This involves both technical and organizational measures. From encrypting data at rest and in transit to ensuring AI models and processes comply with international data protection regulations.
Application: Generative AI and LLMs, given their complexity and the scale of data they handle, must be at the forefront of maintaining data security. This involves regular patching, adopting best security practices, and staying updated with regulatory changes. For businesses, this also means ensuring that data processing agreements, consent mechanisms, and data rights management systems are up-to-date and robust.
Responsibly Getting the Most from Generative AI and LLMs:
Generative AI, including LLMs, promise tremendous value in various applications– from content creation to decision support. However, the balance between utility and privacy is crucial. Here's how one can responsibly harness their potential:
Clear Consent Mechanisms: Before utilizing user data, always have clear and understandable consent mechanisms in place. Users should know how their data will be used and for what purpose.
Regular Model Updates: Continually update models to ensure they're not only optimized for performance but also for privacy and security. Regular reviews will catch potential vulnerabilities or bias.
Limit Data Retention:
Set clear policies on how long data will be retained. Once its utility has been exhausted, securely delete or anonymize it.
Educate the End-Users: Users of LLMs and generative AI systems should be educated
about their capabilities and limitations. This ensures realistic expectations and responsible usage.
Conclusion
Safeguard Your Organization With Trava Cybersecurity Tools
The positive impact of AI on society and our daily lives is undisputed. However, as new AI apps emerge, concerns regarding potential AI cybersecurity risks are also growing. AI technologies such as ChatGPT have various challenges and disadvantages ranging from biased or wrong content to cybersecurity vulnerabilities. The tips we have shared here can help protect yourself and your organization. If you have further questions, contact Trava. We provide expert guidance and tools to increase your resilience against ever-rising cyber-attacks. Contact us today to schedule a free consultation.