3GIMBALS’ Post

Hi #dataengineer, Ever wondered about mixing #AI with #data #engineering? Well, I got a little taste of it recently. We were working on a project where we used a chat interface to pull data. The results? Pretty amazing! But here’s the snag we hit: how do you keep data secure? Especially when you're dealing with sensitive info like PII (personally identifiable information). You see, if you let an AI directly access all the data, you need a solid plan to make sure people only see what they’re supposed to see. Good news is, we found a simple fix to keep those Role-Based Access Control (RBAC) policies in check. So, if you're diving into AI for data engineering, make sure your tool can handle security stuff too. Would love to hear if anyone else has tackled something similar! Cheers, P.S. Oh, and make sure your AI knows not to take “drop database” too literally—some commands are better left unfollowed!

Linked – AI data readiness: C-suite fantasy, big IT problem This might seem like a new problem, but it's an old problem that shows up when trying to do something new.  There is a lack of a strategy for data security and data rot. Too many organizations have punted when deciding what data to keep versus delete and haven't made the hard decisions about implementing data security internally. Now, they want to use AI, but the AI is accessing all that outdated data and exposing the places where security wasn't correctly implemented. So, it becomes an IT problem to fix.  #AI #Data #RecordsManagement #Security

Empower your manufacturing process with data-driven decision-making strategies. +1-760-478-6770 https://marradata.ai/ #data #technology #datascience #business #tech #dataanalytics #bigdata #machinelearning #ai #analytics #artificialintelligence #security #datavisualization

So many aspects of what's discussed in this post about Linea AI completely makes sense to me as a powerfully appropriate application of AI as a core tool in cybersecurity threat detection, risk analysis / assessment, mitigation workflow recommendation engine, and potential event / incident report system. I'm a look into Linea AI and Cyberhaven a bit deeper. I don't really want AI to auto-generate my personal and expressive creative work ... at least not until I learn how to use it at the level of a musical instrument, going beyond the prompt-based input-output factory-like machine.

💥Here's Part II of my three-part post on "Trustworthy AI" **To all my AIGP, AI Governance and EU AI friends ** 👉 ➡ Part I can be found here: https://lnkd.in/erdwPYuA What are the seven key EU requirements for Trustworthy AI? How can we minimize risk according to the general buckets of "Data design", "Model design", and "Loss function?" ⏹REQ 1 - Human Agency and Oversight. The requirement of Human agency and oversight is based on the idea of human autonomy. DATA - training set is curated with diversity and inclusivity in mind. Try to defuse micro-targeting. MODEL - Human agency and oversight can be facilitated by relying on simple models such as linear models with few features or decision trees with small depth. LOSS FUNCTION - include a penalty term in the loss function which can force the trained model to yield predictions that don't change across different mental states of the same user. ⏹REQ 2 - TECHNICAL ROBUSTNESS AND SAFETY. How do we guard against imperfections, like programming errors, quantization noise, power outages, or interrupted communication links and hardware failures? DATA - Use adversarial training to include "perturbed data points" in the training set. OR - prune the dataset using some form of outlier detection. MODEL - Develop qualitative and quantitative measures of continuity and robustness. LOSS FUNCTION - Use a lot of complicated math to "disturb" the model features and the data labels and create an "uncertainty set". #OnlyIfYouLoveRealMath ⏹REQ 3 - PRIVACY AND DATA GOVERNANCE - Do we need DPO inspection or even a DPIA to minimize risk? How do we quantify privacy leakage? Do we need Differential Privacy to introduce randomness or noise to the output? #AIGPExamTopic #DifferentialPrivacy Donna Rinck, Esq. DATA - Carefully select your features used to characterize data points. Use only features relevant for the learning task that don't convey too much information about sensitive attributes. MODEL - Use linear feature learning techniques and shallow decision trees so that each resulting decision contains a minimum number of data points. LOSS FUNCTION - Use a random linear function in addition to your objective loss function. ⏹REQ 4 - TRANSPARENCY - Provide explanations for predictions. Use models that are "intrinsically interpretable" #GoodLuckWithThisOne Inform users when interacting with an automated system, be transparent. DATA - Datasheet for Datasets! Document your datasets - show the composition, collection process, and intended use. MODEL - Model Cards. System Cards. Donna Rinck, Esq. #AIGPExamTopic #EvansFavoriteTopic Similar to datasheets for datasets, model cards provide transparency about the performance of trained models across various demographic groups. LOSS FUNCTION - Augment your training set with "pseudo-label examples" and use #ComplicatedMath to construct new penalty terms for expected loss. I'll sum up EU Requirements 5-7 in a final third post.

The traditional Course of Action (COA) analysis, essential yet painstaking, is in the early stages of being transformed by AI. Recent DOD research initiatives seek solutions and hope to enhance decision-making in complex and high-stakes environments. This is not a solved problem. Reasoning models are in a nascent state. Memory, actions, and agents are still in the early days of maturation. Hallucinations have come way down. There are opportunities and challenges; the time is right to progress in this space. So let’s dive in.

Talent and skills are often the key factors that stand between a CIO’s roadmap and delivering real results. A highly skilled team isn’t just an asset—it’s the foundation of a highly successful IT organization. Curious about how to close the skills gap in your team? Let’s talk about Pluralsight’s AI skill assessments and hands-on sandboxes, and how they empower IT organizations to reach peak performance. #AIsandboxes #skilldevelopment #pluralsight https://lnkd.in/eYVgNbig

I'm excited to share my latest blog post on how we tackled LLM hallucination while building Felix 🤖, FunnelStory's data-access chatbot! Key takeaways: ✨ LLMs can provide inaccurate information, requiring safeguards like system prompts and function prompts. ✨ Chain of thought prompts help guide the LLM's decision-making process. ✨ Robust debugging and logging are crucial for identifying and resolving issues. This blog post explores the challenges we faced, the solutions we implemented, and the next steps we're considering to further enhance accuracy. Read the full blog post here: https://lnkd.in/gKKhxHk7 #FunnelStory #Chatbot #AI #LLM #MachineLearning #ArtificialIntelligence

🌞 Catch Up on Our Webinars This Summer! 🌞 Missed any of our recent webinars? No worries! Summer is the perfect time to catch up on the latest insights and trends in enterprise data management. Start with our popular session, "From Chaos to Context", and discover how to transform your data strategy using AI-driven data discovery and classification. 👉 Watch On-Demand: https://bit.ly/4eXnikM Enhance your data management skills and drive value with our expert-led webinars. Don’t miss out! #DataDiscovery #DataClassification #AI #DataSecurity

Intelligence Center 3.3 offers a range of updates that will elevate your threat intelligence operations. Zooming in on three key areas of improvement and innovation, this release: 1️⃣ Introduces additional data policy features for refined data retention and improved data export via more flexible CSV support. Improvements in source management give administrators greater flexibility in adapting to evolving collection requirements and ensure intelligence contributions are consistently attributed to the correct source. 2️⃣ Further enhances our MITRE ATT&CK support. We've updated the built-in Enterprise framework to the latest 14.1 version and automated the creation of mappings for ingested reports. The introduction of a dedicated search interface for relational queries, and upgraded support for TLP-protocol version 2.0, significantly improve the CTI analyst experience as well. 3️⃣ Heralds the debut of our first Generative AI-powered feature, signaling the start of a new long-term initiative to supercharge intelligence operations with AI technology. The beta AI Report Creator enables analysts to generate comprehensive reports efficiently using Generative AI from market leader OpenAI. Learn more about each of these as well as other enhancements in the blog post: https://lnkd.in/evjvw6sR Get ready to transform your #ThreatIntelligence journey!