Prepared for: Enterprise AI Teams and AI/Security Leadership Based on: Publicly available information and Cursor's provided documentation from:https://www.cursor.com/security, https://trust.cursor.com/faq and https://trust.cursor.com/ Table of Contents Executive Summary Introduction to Cursor Core Security Architecture and Practices AI Request Processing and Data Handling Codebase Indexing: Functionality and Security Privacy Mode: Guarantees and Implementation Enterprise-Specific Features and Considerations Potential … Continue reading Detailed Security and Enterprise Readiness Report: Cursor AI IDE
Category: Data Science
Ship Code 10× Faster: Guide, Don’t Grind—With AI Coding Assistants
1. The Productivity Cliff We’re Ignoring ? In 2025, not pair‑programming with an AI coding companion is like scrolling through Google results page‑by‑page while everyone else fires off one‑sentence queries to an AI search assistant—it technically works, but you bleed hours that snowball into months of lost velocity every year. I learned this the hard way. My … Continue reading Ship Code 10× Faster: Guide, Don’t Grind—With AI Coding Assistants
Building a 10-Q Analyzer: Part 1 | Extracting Financial Insights with AI
Read Part 2 and Part 3 In the evolving landscape of artificial intelligence, combining advanced techniques like Retrieval-Augmented Generation (RAG) and Named Entity Recognition (NER) has opened new avenues for extracting and structuring information from complex documents. This blog delves into the intricacies of building a 10-Q Analyzer—a tool I designed to process SEC 10-Q … Continue reading Building a 10-Q Analyzer: Part 1 | Extracting Financial Insights with AI
Quick Outline for Designing Data Pipelines for Machine Learning Projects
As a Machine Learning Engineer, designing a data pipeline involves ensuring data flow is efficient, scalable, reliable, and optimized for the requirements of ML models. Here’s a structured outline to keep in mind: 1. Data Ingestion Sources and Types: Identify data sources (e.g., databases, APIs, logs, IoT devices) and data types (structured, semi-structured, unstructured). Batch … Continue reading Quick Outline for Designing Data Pipelines for Machine Learning Projects
Understanding Production RAG Systems (Retrieval Augmented Generation)
1. What is RAG ? Retrieval Augmented Generation (RAG), is a method where you have a foundation model, and you have a library of personal documents – this can be unstructured data in any format. Now your goal is for answering some questions from your persona library of docs, with the help of LLM. Enter … Continue reading Understanding Production RAG Systems (Retrieval Augmented Generation)
Data Science Leadership Series – Part 3: Building Great Products by Balancing Product and Engineering Mindsets
I wrote about why we need AI Product Management when building complex data products in a previous post in the Data Science Leadership Series, and I feel this has to be followed by a very important topic that almost every single software development team faces, especially in high growth startups where the speed of innovation … Continue reading Data Science Leadership Series – Part 3: Building Great Products by Balancing Product and Engineering Mindsets
AI Product Management in Cybersecurity: McKinsey’s Three Horizons of Growth ? Flip it for startups, keep it for mega corps
What’s McKinsey’s Three Horizons of Growth ? The McKinsey’s Three Horizons of Growth is a management framework first introduced in the book “The Alchemy of Growth” published in 2000, which has since been referenced by top product strategists to help businesses execute existing business models while simultaneously innovating and pushing the boundaries and creating new … Continue reading AI Product Management in Cybersecurity: McKinsey’s Three Horizons of Growth ? Flip it for startups, keep it for mega corps
The Game Changer: Large Language Models Unleashing the Power of AI in Cybersecurity
Applied Machine Learning and AI in cybersecurity is slowly growing into mainstream industry, not just as an add-on anymore. As threat actors have started automating and streamlining a lot of their attack pipelines using off the shelf Large Language Models, the good actors are essentially forced to catch up with them ! It’s not just … Continue reading The Game Changer: Large Language Models Unleashing the Power of AI in Cybersecurity
Data Science in Venture Capital – An Overview
I have been recently doing some research on applied Machine Learning and AI in Venture Capital (VC) funds, and it's a really interesting rabbit hole to go into, especially if you yourself are interested in either startups (as a would-be founder / employee) or in VCs themselves as to how they work as data driven … Continue reading Data Science in Venture Capital – An Overview
Data Science Leadership Series – Part 2 : How to choose data projects: Core Product Vs Support Consulting Vs Research | Beware of your bottomline
The biggest challenge for data scientists / managers in decision making capacity and one with the biggest consequential outcome for both the business and the data team, I feel, is the part where you say yes / no / let’s modify - to a new data project idea from leadership, or even starting a new … Continue reading Data Science Leadership Series – Part 2 : How to choose data projects: Core Product Vs Support Consulting Vs Research | Beware of your bottomline
Infinite Couch 