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Models

Microsoft launches project perception platform for agentic cybersecurity

Microsoft is set to open its Project Perception platform to the public on 3 August. The initiative centres on a new cybersecurity-specific model named MAI-Cyber, designed to provide agentic security capabilities for enterprise environments.

Why it matters

For teams building production AI, this shift towards specialised agentic models for security signifies a move away from general-purpose LLMs in sensitive domains. It highlights the importance of domain-specific fine-tuning for reliability in automated threat detection.

Key points

  • Public launch of Project Perception scheduled for 3 August
  • Introduction of MAI-Cyber, a model purpose-built for cybersecurity tasks
  • Focus on agentic security to automate complex defensive workflows
  • Industry focus on vendor-reported performance metrics for the new model

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OpenAI disbands preparedness team responsible for assessing catastrophic ai risks

OpenAI has reportedly dissolved its preparedness team, the group tasked with evaluating and mitigating potential catastrophic risks from advanced AI models. This internal restructuring follows several high profile departures from the company safety and alignment divisions.

Models

Deepseek raises V4 API pricing by up to eleven times ahead of reported IPO

DeepSeek has implemented a substantial price increase for its V4 API, with some costs rising by up to eleven times as of 16 August 2026. This shift signals the end of the low cost strategy the company utilised to disrupt the market in 2025. The adjustment coincides with industry reports suggesting the firm is preparing for an initial public offering.

Models

Writer releases enterprise-optimised GLM-5.2 variant with token-saving technology

Writer has launched a new variant of the open-source GLM-5.2 model, specifically post-trained to meet enterprise reliability standards. This release introduces a specialised harness designed to significantly reduce token consumption, directly addressing the high operational costs associated with large-scale model deployment.

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