Everyone thinks going private with AI means going secure automatically, but that's not how it works. In this episode Zareef breaks down why private AI deployments only shift the responsibility of security onto your own organization instead of guaranteeing it, and what you actually need to watch out for if you're running your own setup.

• Private AI means your data and operations stay within your own network and never leave your organization's boundaries
• Private doesn't mean secure, it just means the responsibility for security now belongs to you
• Common misconfiguration risks include open ports, insecure storage, and undefined inbound or outbound connections
• Weak authentication and excessive user privileges are frequent problems, ignoring the principle of least privilege
• Unpatched operating systems, outdated libraries, and old container images can all introduce vulnerabilities
• Prompt injections and malicious documents in RAG systems are real risks to watch for
• Sensitive information can leak into logs, caches, or backups if not handled properly
• Human error remains one of the weakest links, whether accidental or intentional misuse of access
• Insecure integrations with external services and poor patching, monitoring, or incident response add more risk
• Despite the risks, private AI still offers major benefits like data privacy, better performance for large datasets, and full control over your systems
• The key takeaway is that private AI can be secure and has to be made secure, but it is never secure by default