LLM Data Security: The Unaddressed Risks and Consequences

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View profile for Nithin Goud

Full Stack Developer

What We're Missing in LLM Data Security 🚨 Pain Points We're Ignoring: - Basic PII masking ≠ real protection—LLMs memorize & leak sensitive data anyway - Context inference—models connect dots to reveal identities even from "anonymous" data - Prompt injection attacks bypass all traditional security measures - Training data poisoning implants harmful content directly into models - Output filtering deficiencies enable leakage of sensitive information in responses The Real Impact: - GDPR/HIPAA violations → massive fines - Identity theft & fraud stemming from compromised PII - Damage to reputation and erosion of customer trust - Business email compromise resulting in financial setbacks Bottom Line: Traditional cybersecurity wasn't built for LLMs. We need zero-trust data pipelines, not just surface-level fixes. #DataSecurity #LLM #AI #Privacy #CyberSecurity

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