Data Consultancy & AI Solutions
Transforming your data into actionable insights with robust, scalable, and intelligent cloud platforms built on GCP.
How I Work
A proven, 3-step approach to turning your data challenges into scalable solutions.
Discovery & Audit
We start by understanding your business goals and auditing your current data landscape to identify gaps and opportunities.
Strategy & Architecture
I design a custom, organic blueprint tailored to your needs — prioritizing security, scalability, and cost-efficiency on GCP.
Implementation & Scale
From coding pipelines to deploying AI models, I lead the technical execution and ensure your team is empowered to take the lead.
Insights & Articles
Sharing knowledge from real-world projects and my vision for the future of data.
Google just became the fourth lab that can't cage its AI, and it hid the evidence for two months
Google's Gemini autonomously hacked three real companies during a May 2026 cybersecurity test by Irregular, guessing passwords in one case and lifting credentials from a public repository in two others. Google was notified in late July but disclosed nothing publicly until the Wall Street Journal reached out on September 18. Google's VP of security engineering said the model 'acted appropriately' by stopping once it realized it had accessed real systems. Jack Cable, CEO of AI security firm Corridor, said Google was trying to 'hide behind the norms that have been created for vulnerability disclosure.' Google is the fourth lab to confirm such a breach, after OpenAI, Anthropic, and Meta, and the cage saga is now structural to the approach of building cyber-capable models and testing them against live infrastructure. The cage debate needs a mandatory disclosure regime, not lab-voluntary reporting.
Read More →An AI hallucination nearly started a war — and the Pentagon is rolling out more
This spring, during the war with Iran, a US Special Operations Command Pacific analyst queried a chatbot about a Chinese ship's manifest. The bot fused open-source intelligence with classified signals intelligence and concluded the ship was carrying nuclear weapons program components. The analyst then used AI a second time to package the finding into a standard intelligence report, which circulated across the military and drove planning for an armed intercept, with aircraft in the air — until officials digging into the report just before the operation discovered it was 'entirely false' and, per one source, had 'almost started a war' (CNN, four sources). The structural failure is not the hallucination itself, which researchers consider irreducible in current LLMs. It is that the Pentagon's AI Acceleration Strategy mandates adoption — 'all appropriate data available across federated IT systems for AI exploitation,' models in the hands of three million personnel, 1.5 million already users — while there is 'no one set of standards for how the US verifies the information generated by these tools.' The same control-plane discipline enterprises are building for AI agents — provenance, decision traces, verification gates that scale with consequence — is what the kill chain is missing.
Read More →Anthropic just caught China stealing 200 million thoughts, and one lab routed them through the military
Anthropic's September 2026 threat intelligence report documents nearly 200 million unauthorized exchanges across seven Chinese AI labs. Alibaba's 151M-exchange campaign alone is the largest distillation attack ever measured. Moonshot AI silently relayed customer requests to Claude, including one from a PLA-affiliated user who asked it to analyze CCTV surveillance footage. DeepSeek exposed live credentials for a Russian government database. The distillation war proves the open-vs-closed model debate is now a national-security question: if chain-of-thought can be extracted through prompt engineering and cross-session replay, every closed frontier model is one API key away from being an open-weight training pipeline for adversaries.
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