Your AI model may be secure. Your AI supply chain might not be. Think about everything surrounding a modern…
samuel watua Soita — Full Stack Software Engineer
Your AI model may be secure. Your AI supply chain might not be.
Think about everything surrounding a modern AI application.
Open-source packages.
Models.
Plugins.
MCP servers.
APIs.
Vector databases.
Cloud services.
Third-party tools.
Container images.
CI/CD pipelines.
Every connection introduces another potential attack path.
This isn't theoretical.
The cybersecurity community has already seen supply-chain compromises involving software used in AI environments, while Microsoft recently reported a sophisticated campaign involving the compromise of ConnectWise and the use of AI-assisted tooling.
The lesson is simple:
Your application's attack surface is larger than your application's code.
If your AI system can call a tool, that tool becomes part of your security boundary.
If it can access an API, that API becomes part of your attack surface.
If it depends on a package, that package becomes part of your supply chain.
Security teams therefore need to start asking:
“What does my AI application trust?”
And then:
“What happens if that trust is compromised?”
AI security isn't only about securing the model.
It's about securing everything the model can reach.
#AISecurity #Cybersecurity #SupplyChainSecurity #AgenticAI #MCP #CloudSecurity #AppSec #DevSecOps #ThreatIntelligence #InfoSec
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Topics: #devsecops, #agenticai, #cloudsecurity, #mcp, #infosec, #threatintelligence, #appsec, #supplychainsecurity, #aisecurity, #cybersecurity