Hello everyone. I'm Balaji today. I'll be talking about securing l ls. Let's get into the topic. So today I'm dying diving into a pressing issue in artificial intelligence securing launch language models. To prevent leaks on sensitive code, critical data, and valuable intellectual property, these leaks can undermine business and trust. A, as the LMS become indispensable, tools across the Indus industries from tech to healthcare, protecting them is no longer optional. It's a necessity. So let's start this journey to understand and fortify. artificial intelligence systems against these risks. So what are s elements are cutting edge AI systems designed to understand and generate human-like text with remarkable accuracy. Take GPT with its staggering. One 75 billion parameters. It's a pioneer in natural language processing. These models drive tasks like content creation, language translation, and even customer support automation. They act like digital's, linguists, interpreting and crafting languages in ways that revolutionize technology interactions. So the problem, so the real problem that we're facing right now with LLMs is data leaks. The thing is the data leaks happens even without knowing that it happens. So despite these powers of L lms, LMS can leak sensitive information in several sneaky ways. Leaks can stem from training data where private details like names or emails might linger unnoticed. All from prompts where users accidentally input this data. And also from model, inversion, a technique attackers used to reverse engineering data from LM outputs picture. And LM as a leaky bucket sends sensitive code data and IP spill out, unchecked, threatening privacy breaches, intellectual property theft and competitive losses. So I ask, are you, are your A tools truly secure against these vulnera vulnerabilities, data leaks and their impact? So the real world example that we can go with is with Samsung in 2023, the employees inadvertently. Leaked proprietary source code by using Chad GBT exposing trade secrets to potential rivals. A healthcare provider lost pat, patient data through L-N-L-L-M, bridging trust and regulations. A startup saw its intellectual property vanished due to sloppy prompt handling. IBM picks the average database cost at 4.45 billion. Which is a really hefty price. The stable lists, these incidents, their fallout and lessons like enforcing strict input policies and sanitizing data to avoid these costly mistakes. Technologies for securing L lms. Thankfully a new technologies can share. can. Shore up the LMS against these leaks. Differential privacy, add statistical noise to the data set, masking the individual details while keeping overall pattern consistent, which is ideal for privacy. Second option is federated learning, which trains models on decentralized devices like phones and servers, so the raw data never leaves its source. The third option is homomorphic encryption and emerging model that lets the LMS process encrypted data without even decrypting it. So it's a true frontier. Things of these, things like these act as a study ball and then it helps, for the, companies to protect their data. Data privacy solutions for L lms. So beyond these core technologies that are in use that we can use, we have got specialized tools also in play data. Privacy walls like those from Sky Flow Act as secure. repositories tokenizing the data, like credit card numbers. Before LMS really cease them. LM Shield developed by patented AI that filters user inputs in real time, catching personal and proprietary info before it really leaks. Imagine a digital vault. You were secrets. Stay locked tight inside, and only safe anonymous tokens can reach the. These solutions are really pivotal for complaints and also for safeguarding our assets, best practices for using LLM securely. So that is tech technology that is available right now, but that alone won't cut it, that are also, complaints that we need to make sure. so the best practices that could be used. Or, sanitizing the training data and restricting access to limited people and not to everyone, and who is training the data and who can use the data. And also conducting regular audits and training employees to handle, a responsibly. So these four things will definitely make sure that the best practices are implemented in, each company that could make LLM secure. Next is, regulatory, complaints. regulatory complaints talks about, how standards can be set, which can, prevent. data leaks. So GDPI from, European Union in insists on data rights, which becomes tricky when models can memorize this, data. HIPAA safeguards, health information in the US demanding highend cloud protection for patient records. CCPA gives California consumers, control over their data. Pursuing for transparency, the staple ties. These, laws of strategies like differential privacy of A-G-D-P-R walls for HIPAA complaints, isn't just a, just a checkbox, right? It's the legal foundation for safeguarding the AI deployment. next is implementing a secure alarms. So since we have seen, everything right now on different ways we can, Secure lums. We can see how to implement it. So to put it in practice first, assess, assess your data, what's sensitive and what needs, guarding. Then select the tools like privacy walls or shields to protect it. Set up, access controls to limit the exposure to the data. Train your staff on secure, a use and audit systems regularly for any weak spots. This flow chart maps, out your path to a leak proof LLM. It's a clear actionable plan that turns security theory into real world results. Future trends and conclusion. So looking at future trends, privacy, preserving, AI is the need of the heart, and that is what we see in the future. And also there will be new protocols that could be implemented, which can act as, secure a on its own secure agents that can help, to preserving the data on its own. In summary, secure L LMS will help to protect the assets and, the companies need not have the fear. If their intellectual property is really lost to any of the other companies and potential rivals. the real priority right now is to secure our lums to make sure that the data that we want is not left out in the open so that anyone can access it as they want to. Thanks for listening.