AI Application Security
Securing AI-Powered
Applications
AI introduces new attack vectors. Prompt injection, model extraction, data poisoning—threats that traditional AppSec doesn't cover. I help teams build AI applications that are secure by design.
AI-Specific Security Concerns
Prompt Injection
Preventing malicious inputs from hijacking AI behavior
Input validationOutput filteringSandboxed executionRate limiting
Model Security
Protecting model weights, training data, and inference endpoints
Access controlsEncryption at restSecure inferenceAudit logging
Agent Boundaries
Containing what autonomous agents can access and modify
Least privilegeAction allowlistsHuman-in-the-loopKill switches
Data Leakage
Preventing sensitive data from appearing in AI outputs
PII detectionOutput scanningContext isolationRetention policies
My Approach
1
Threat Modeling
Identify AI-specific attack vectors for your application. What can go wrong when an LLM is in the loop?
2
Defense in Depth
Layer security controls. Input validation, output filtering, monitoring, and containment.
3
Continuous Monitoring
AI systems behave differently over time. Set up logging, alerting, and anomaly detection.
Building an AI Application?
Let's make sure it's secure from day one.
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