🚀 10. Automating Trust in Predictive AI Models
10 October 2025
After weeks of integrating Jenkins + MLflow + Docker, I now have a fully automated AI Model Validation Framework running end-to-end.
Read more →🧠🔐 9. AI Security Architecture — The New Blueprint for Trust
10 October 2025
AI is rewriting how organizations think about security architecture. It’s no longer enough to secure infrastructure — we must secure intelligence itself.
Read more →⚙️ 8: Hardening ML Inference: A Deep Dive into Securing Models & APIs
29 September 2025
A Deep Dive into Securing Models & APIs In many ML projects, deploying a model is the easy part — the real challenge is making sure it runs securely in production. I recently finished a project where I locked down...
Read more →🔒 7: Privacy-Preserving AI
25 September 2025
Privacy-preserving AI is about protecting sensitive data while still extracting valuable insights from it. This ensures models are trained, deployed, and used without compromising individual privacy. A core principle here is data minimization (GDPR) — only collect and process the...
Read more →🛡️ 6: Adversarial Prompt Attacks on LLMs
21 September 2025
Large Language Models (LLMs) bring incredible capabilities — but they’re also vulnerable to prompt-based adversarial attacks, where carefully crafted inputs manipulate the model into breaking rules or leaking sensitive information.
Read more →🔐 5: Model Inversion & Inference Attacks (Stealing the Data)
17 September 2025
While model extraction steals the model, inversion and inference attacks aim to steal the data — often the most sensitive asset an organization has.
Read more →🔎 4: Model Extraction (aka “Model Stealing”)
13 September 2025
In the fourth post of my 7-part series on securing AI systems, I dive into Model extraction attacks. Model extraction attacks seek to replicate a deployed model’s behavior (and sometimes its parameters) by repeatedly querying it and using responses to...
Read more →🔐 3: Evasion Attacks — Fooling Deployed AI Models
09 September 2025
In the third post of my 7-part series on securing AI systems, I dive into evasion attacks — how attackers manipulate inputs after deployment to bypass AI models and what organizations can do to defend against them.
Read more →🚨 2: Poisoning Attacks — When Hackers Train Your AI
05 September 2025
In the second post of my 7-part series on securing AI systems, I dive into poisoning attacks — how attackers compromise AI models before deployment and what organizations can do to defend against them.
Read more →🚀 1: Building a Strong AI Security Baseline
01 September 2025
As organizations race to integrate AI into their products, security often lags behind — leaving critical models, data, and APIs vulnerable. Here’s a practical baseline checklist to secure your AI systems from the ground up:
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