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We are hiring a Head of Software Engineering & Product Commercialization. A key role as we deploy our technology. Our team built something rare: a cryptographically attested, inference platform that regulated industries such as banks, insurers, asset managers, defense primes deploy without losing data sovereignty. And we have developed and fused agentic ai safety using a highly advanced and novel approach to ensure agents run and behave as intended by the organization. We are looking for industry experts that can deploy and fine tune the road map based on co-development projects with our partner organizations in the financial sector and government in the US and Europe. Send a message directly if you’re interested. It would be a pleasure to speak.

I recently installed a heat pump in my home — a solution that offers high energy efficiency, significant cost savings, and contributes to environmental sustainability, with the Novair solution from PPC. # PPC # Novair

Fine-tuning a model on proprietary data is three separate data exposures. Most legal teams reviewing the agreement only know about one. When an enterprise fine-tunes via OpenAI's interface, uploading data in the required format, three things happen simultaneously: 1. The data is copied to a file on your own system. 2. It is uploaded to OpenAI, who now holds that training file. 3. It is embedded into the fine-tuned model itself, where it can be extracted through inversion attacks using nothing more sophisticated than persistence and open-source tooling. The enterprise agreement restricted how the data could be used for training. It did not prevent any of the downstream exposure risks. Then there is the log problem. You can set log retention to zero, but retention settings are not absolute. A court order can override them immediately, as OpenAI discovered when the New York Times lawsuit required preservation of prompt logs for legal review despite stated retention policies. Those logs contain more than user prompts. In many enterprise deployments, they may also capture the full RAG context, including every sensitive document retrieved to generate a response. At the same time, AI-related risk is becoming increasingly difficult to insure, with carriers beginning to carve out exclusions for AI-driven incidents as exposure grows. A contract is not a substitute for technical protection. Neither is a retention policy. What, exactly, are your legal and security teams reviewing in your provider agreements?

I had the opportunity to attend the FTTH Conference 2026 in London. It was a really interesting experience, with great discussions around the evolution of FTTH networks and how customer experience is becoming more and more important. Glad I can be part of this through my role as a Senior Product Manager at PPC Fiber and bring some new ideas back to the team. #FTTH #Telecom #ProductManagement #DEIFiber

The first wave of AI security products reached market quickly. Most of them started life as perimeter tools, endpoint solutions, or DLP platforms with an AI feature layer added on top - which means none of them were built for the problem that agentic AI actually presents. None started from the problem that actually needed solving: how do you protect data during inference, and how do you detect when an agent's goals have been corrupted at the semantic layer? These are not edge cases, they are the primary attack vectors for agentic AI in 2026. Twenty years across Intel and Cisco teaches you something that no threat landscape report will - the companies that got security right never started from a product. They started from the threat model, and built backwards. And while those that tried to retrofit an existing solution onto an emerging threat didn't fail dramatically, they fell behind and couldn't catch up. Confidential Core AI was built from a clean slate - ground-up for agentic security. Four pillars in one platform: 1. Adversarial attack protection 2. Real-time goal alignment 3. Model and data confidentiality during inference 4. Sovereign deployment. This is not just our view. When independent security researchers evaluate the available approaches, confidential compute consistently comes out as the most comprehensive. Confidential compute is the only approach that can run any model, any vector database, and any AI framework inside a hardware-enforced boundary without architectural compromise. The challenge researchers consistently flag is not technical viability but the complexity of monitoring agentic AI. It is hard to set up, hard to verify, and easy to misconfigure. That is the problem we are solving. Enterprises that recognize the nature of the problem will build from the threat model. They will secure the inference layer before the first agent is deployed at scale. The ones that don't will make that discovery later, under less favorable circumstances.