Jamf launches native AI governance and control plane for managed Mac fleets

Jamf has introduced a first of its kind native AI control plane for Mac devices, initially launching in Australia and New Zealand. The solution provides enterprise teams with visibility and governance over the AI tools used across their managed hardware.

As developers increasingly use local and cloud based AI tools, organisations require robust oversight to prevent data leakage. This native integration allows teams to maintain security standards without hindering the performance of AI development on Apple silicon.

  • Offers granular visibility into AI application usage across enterprise Mac fleets
  • Facilitates compliance by governing how third party AI tools interact with company data
  • Supports the secure adoption of generative AI technologies within regulated industries
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Policy

Anthropic reveals Claude leads over a quarter of its internal AI research

Anthropic has announced that its Claude model now spearheads 26 per cent of the company's internal artificial intelligence research. This shift demonstrates a move towards self-improving systems where the model contributes directly to its own architectural and safety developments as an active researcher.

Tooling

China shifts focus towards national security and systemic risks in artificial intelligence

Chinese policymakers are pivoting their regulatory focus from immediate issues like deepfakes to broader national security threats posed by artificial intelligence. This shift follows internal warning shots regarding the potential for advanced systems to compromise state stability or critical infrastructure. The move aligns Beijing more closely with global concerns regarding sustained safety and systemic vulnerabilities in large scale deployments.

Models

Local LLM deployment reduces AI operating costs to one per cent

A recent implementation using local large language models and the Jev framework has demonstrated a significant reduction in AI product operating costs. By migrating workloads from expensive cloud APIs to local infrastructure, developers achieved a cost reduction of 99 per cent, moving from 400 million to 4 million units.

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