Crux (YC W24)’s Post

"Will my data be at risk if I build an LLM application?" This is what we hear in almost EVERY conversation with product & data leaders. These are real, valid concerns. As enterprise leaders, your organization needs to be de-risked from all privacy breakdowns at the LLM-level, infrastructure-level or at the application-level. But this shouldn't come in the way of innovation, right? That's why we're building Crux with a privacy-first foundation in place so that you can focus on discovering real use cases, while we take care of the rest. Find out more about how Crux is tackling privacy concerns head-on: https://lnkd.in/grfPv2pj #LLM #Privacy #GenerativeAI

Technological advancements have simplified the process of gathering and storing unstructured #data from various sources. Research shows that upwards of 80% of any company’s data is deemed unstructured. Consequently, businesses are now storing significantly more data to generate meaningful business insights. Because of this massive amount of data from so many sources, it is critical that companies have a holistic view of information to prevent data misuse through LLMs across all internal sources. Former ways of encrypting and masking data in databases, assuming that user identities cannot be derived from secondary attributes, is no longer sufficient. At Secuvy, our mission is to provide a comprehensive approach to data security, privacy, and governance. Our unique capability to comprehend and establish connections among various data categories offers enhanced insight into legal responsibilities and security risks, a value that is indispensable for any enterprise. Read more about the connection of #LLMs to unstructured data and the tools available to help with LLM governance here: https://hubs.li/Q02t0rBy0 #dataprivacy #unstructureddata #datagovernance #datadiscovery #AI #machinelearning

More due diligence is needed in adoption of generative AI apps 💻 The adoption of generative AI applications requires careful consideration and due diligence due to several factors: 📣 Legal and Ethical Issues: Because generative AI may create content that closely resembles content created by humans, there are legal and ethical issues around the possible exploitation of this technology to create offensive or fraudulent content, deepfakes, or fake news. Clear policies and moral frameworks must be established by organizations to ensure that generative AI technologies are used responsibly. Legal concerns including copyright violations and intellectual property rights also need to be taken care of. 📣 Data security and privacy: Large datasets, which may contain private or sensitive information, are frequently needed for training in generative AI applications. To defend against unauthorized access or misuse of data, must employ strong data protection methods, such as data anonymization and encryption in order to maintain compliance with data privacy requirements, such as the CCPA or GDPR. 📣 Quality and Bias Mitigation: The outputs generated by generative AI models may have biases or inaccuracies that are present in the training set of data. Companies must evaluate the dependability and quality of material that is generated and put policies in place to reduce bias, such as algorithmic transparency, bias detection, and the curation of various datasets. 📣 Technical know-how and Infrastructure : Developing and overseeing generative AI applications calls on specific technical know-how and infrastructure, such as deep learning frameworks, high-performance computer resources, and qualified data scientists. To ensure the successful deployment and operation of generative AI systems, organizations must either work with external specialists or engage in training and development to establish internal capabilities. 📣 Regulatory Compliance: The use of generative AI applications may be more closely scrutinized by regulators in regulated sectors like healthcare, banking, or the automobile industry. 🔔 Collaboration between stakeholders, including policymakers, industry experts, and technology providers, is essential to foster responsible and sustainable deployment of generative AI technologies. #it# network #itmanagedservices #data #applications Get Connect - https://lnkd.in/dgRH58_W

According to a report by IBM, it cost the world USD 4.45 million due to data breaches, a sharp 15% increase over 3 years. Data privacy has become a critical concern worldwide, with governments, businesses, and individuals becoming more conscious about protecting their personal and proprietary information and this shouldn’t come as a shock since 2023 saw a 20% increase in data breaches globally. But what does this mean for Businesses? For businesses, this means a fundamental shift in data handling practices, transparency, accountability, and robust security measures. -  Implement strict security measures to protect against data breaches, ensuring the confidentiality and integrity of the information they handle. -  Explicit consent from individuals before processing their data, making it harder and longer to collect, use, and share data. How does Synthetic Data help? Synthetic data is another innovative approach to data privacy, involving the creation of artificial datasets that mimic the statistical properties of real data. By generating fictional but realistic information, organizations can conduct analyses and share insights without exposing any actual sensitive information. Source: 1. https://shorturl.at/jpIU2 2. https://shorturl.at/DJZ39 Report by Gartner: https://shorturl.at/dhjxD Betterdata helps you access data 10x quicker, learn more at: betterdata.ai Read a brief guide on Synthetic Data: https://lnkd.in/dZUJBB6e Request a demo: https://lnkd.in/dBiDwz-P #syntheticdata #artificialintelligence #generativeai #Data #AI #dataprivacy #dataprotection #dataprotectionlaw

Last week, we asked our LinkedIn community about their main concerns regarding the use of Large Language Models (LLMs) in everyday business applications. The results are in, and it's clear that Data Privacy, Bias, & Security are the top priorities for business leaders like you. In light of these concerns, here are some key considerations for ensuring the responsible use of LLMs: 1. Following Data Privacy Standards: Implementing and adhering to robust data privacy standards is crucial. This includes ensuring data encryption, secure storage, and compliance with relevant regulations such as GDPR and CCPA. 2. Consent Taking Process: Utilizing a transparent consent-taking process for data usage, especially for further content generation, helps build trust with users and ensures compliance with privacy regulations. 3. Diverse Training Data: Using diverse training data for LLMs helps mitigate bias and ensures that the models provide fair and accurate results across different demographics. 4. Regular Evaluation: Regularly evaluating LLMs for bias and performance helps maintain their effectiveness and ensures that they continue to meet business needs while mitigating risks. 5. MFA Implementation: Implementing Multi-Factor Authentication (MFA) adds an extra layer of security, reducing the risk of unauthorized access to LLMs and sensitive data. 6. Industry Standard Protocols: Adhering to industry-standard security protocols and best practices ensures that LLMs are deployed in a secure environment, reducing the risk of data breaches. 7. Utilizing Multiple LLMs in One Environment: Utilizing multiple LLMs in one environment can help restrict unwarranted access and potential data breaches, ensuring a higher level of security and control over the use of these models. As business leaders, it's crucial for us to prioritize data privacy, mitigate bias, and bolster security in our AI initiatives. By addressing these concerns head-on, we can foster trust, drive innovation, and create a more responsible AI ecosystem. Thank you to everyone who participated in the poll. Your insights help us shape the future of AI in business! Rajesh Sanakkayala Pratap Sanakkayala Kalyan S Ram Nallanthighal Dwaipayan Mitra Suresh Sanakkayala Nizar Ahamed Rajesh Jonnadula Ambarish Desai Venkata Aditya Reddy Ambati Vivek Nair Santanu Mukherjee #DataPrivacy #BiasMitigation #Security #LLMs #AIInBusiness #Machint

Is your business still trying to protect your data when using Gen AI? We are seeing in the news where businesses and employees are using generative artificial intelligence (Gen AI) applications to review code, write documents, and gather what they think is credible information. The privacy and protection of your data along with the validity of AI generated content is still questionable – putting your business at risk. There are issues of AI being used to debug code while exposing intellectual property (IP) and inauthenticated data being used as legitimate work. The integrity and protection of your data is still work in progress. This Dark Reading article addresses how your data is a prime target for threat actors. The American Privacy Rights Act of 2024 (APRA) is the most comprehensive proposed national legislation defining privacy for Americans to date. Businesses are going to be held liable to comply with the APRA for their audits and reporting practices of data privacy policies. Your cyber-resilience strategy must be updated and written to comply with these new mandates. The actions and enforcement of your strategy should be focused on preparing for 2025 data privacy and AI-cybersecurity challenges. There are Gen AI-applications and tools available to protect your enterprise data. However, it will require your business to make proactive intentional investments in your culture, applications, and AI-technologies to protect your organization’s data in the age of Gen AI.   #genai #dataprivacy #aicyber #cyberresilience #cybersecurity #continuumsstrategies

Is your business still trying to protect your data when using Gen AI? We are seeing in the news where businesses and employees are using generative artificial intelligence (Gen AI) applications to review code, write documents, and gather what they think is credible information. The privacy and protection of your data along with the validity of AI generated content is still questionable – putting your business at risk. There are issues of AI being used to debug code while exposing intellectual property (IP) and inauthenticated data being used as legitimate work. The integrity and protection of your data is still work in progress. This Dark Reading article addresses how your data is a prime target for threat actors. The American Privacy Rights Act of 2024 (APRA) is the most comprehensive proposed national legislation defining privacy for Americans to date. Businesses are going to be held liable to comply with the APRA for their audits and reporting practices of data privacy policies. Your cyber-resilience strategy must be updated and written to comply with these new mandates. The actions and enforcement of your strategy should be focused on preparing for 2025 data privacy and AI-cybersecurity challenges. There are Gen AI-applications and tools available to protect your enterprise data. However, it will require your business to make proactive intentional investments in your culture, applications, and AI-technologies to protect your organization’s data in the age of Gen AI.   #genai #dataprivacy #aicyber #cyberresilience #cybersecurity #continuumsstrategies

What if organisations AND individuals use Signatu Data Privacy Chatbot as their agent? Organisations request consent. Individuals control when, from whom and for what they receive consent requests, or they can themselves initiate a permission. Based on consent, data is shared. Both parties get consent records and/or consent receipts. The chatbot run on the same metadata standard and thus understands what both parties express and can therefore automate data sharing. The chatbot automatically generates required compliance documents for the organisation. Would you like that?

❕Do you know what is happening to your personal data? ❕ Every day our inbox is littered with emails from companies notifying us of their new terms of service policies. While it is tempting to delete the emails and move on to the next task, we encourage you to review those agreements. 🔍 Especially with the increase of artificial intelligence tools, knowing how your data is being used is helpful. These terms outline crucial details about data collection, usage, and privacy practices. By understanding them, you can make informed decisions about your online presence. 1️⃣ Protect Your Privacy: Your data matters. Take control by understanding how it's handled and shared. 2️⃣ Empower Yourself: Gain insight into your rights and responsibilities. Understanding terms helps you navigate digital spaces with confidence and clarity. 3️⃣ Navigate AI Usage: Understand how platforms use AI responsibly. Terms and conditions reveal their commitment to ethical practices. Don't let the fine print slip by unnoticed! Take a moment to review those terms and conditions. #Privacy #DataPrivacy #OnlinePrivacy #DigitalRights #DataSecurity #Transparency #ArtificialIntelligence

Recently, I had a conversation about data leaks with researchers from UC Berkeley. A key question arose: Can a binary result from a #ML model be a privacy concern? Here's a common notion: A model that classifies data, as opposed to exposing raw training sets, inherently safeguards privacy. It aggregates information without returning identifiable data - seemingly a privacy win. But is this enough for robust privacy or #dataprotection? The answer is nuanced. If privacy simply means not disclosing identifiable information, many classification models seem fine. However, privacy is often more complex. People view various non-unique data as sensitive. Thus, even classification models might inadvertently reveal sensitive information. The takeaway? While moving from raw data sets to aggregated model outputs is a step forward in privacy protection, we must critically evaluate if our #AI tools align with the broader, more nuanced expectations of privacy and #datasecurity.