AI Security
Local LLM Deployment: Enterprise Risks
Running LLMs on local hardware eliminates some risks and introduces others. A clear-eyed look at the enterprise risk profile of on-premise and on-device model deployments.
Aug 12, 20257 min read
Deep dives, practical guides, and incident analyses from engineers who build Safeguard. No fluff, no vendor FUD — just what you need to ship secure software.
Running LLMs on local hardware eliminates some risks and introduces others. A clear-eyed look at the enterprise risk profile of on-premise and on-device model deployments.
The Model Context Protocol is transforming how AI agents interact with tools, but it introduces new attack surfaces. Here is what security teams need to understand.
As AI models become critical software components, the need for AI-specific SBOMs and model cards grows urgent. How the industry is extending supply chain transparency to machine learning pipelines.
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AI agents that can execute code, browse the web, and manage infrastructure are proliferating. The security implications of these autonomous frameworks demand scrutiny.
A practitioner's methodology for using LLMs to augment — not replace — traditional bug discovery workflows, with patterns that hold up under real review load.
Anthropic's Model Context Protocol standardizes how AI models interact with external tools. The security implications for software supply chains are significant.
Prompt injection is not just an application vulnerability. When LLMs process content from the software supply chain -- package descriptions, README files, commit messages -- injection becomes a supply chain attack vector.
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