AI risk belongs in the boardroom.
A useful discussion starts with decisions and consequences, rather than a list of model vulnerabilities.
Read the field noteField notes
Perspectives on securing AI.
Notes on trust boundaries, agentic security, and executive oversight — written for people who have to make decisions about systems they did not build.
An intelligent agent still needs limits.
Permission, approval, and recovery should be designed before autonomy expands.
Read the field noteWhen content becomes command.
Why AI applications need a firm boundary between information to read and instructions to follow.
Read the field noteFinancial AI
Finance is all just data. Data in – processing –> data out This is the perfect use case for AI. AI systems …
Read the field noteAI discovers different pathways on it’s own
Nick Bostrom outlines a way that AI may discover an unanticipated way to accomplish a set goal E.g. – if it is…
Read the field noteDeep Neural Nets using Tensorflow
TensorFlow Overview TensorFlow is an open-source machine learning framework developed by the Google Brain team. It provides a comprehensive ecosystem of tools,…
Read the field noteAI Recommender Systems
Key types of filtering based on recommender systems include: 1. Collaborative Filtering Collaborative filtering is a method used to make automatic predictions…
Read the field noteClassification Models in Machine Learning
1. Logistic Regression Logistic regression is a statistical method for binary classification problems. It models the probability that a given input belongs…
Read the field noteNeural Network Architecture and Applications
1. Neural Network Architecture The basic structure and components of neural networks. Key points include: Layers: Neural networks are composed of layers—input,…
Read the field noteAI Cybersecurity
Q: How do Large Language Models (LLMs) contribute to cybersecurity? A: LLMs leverage extensive data to understand security threats and communicate predictions…
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