Ai Security
AI Security
A transparent roadmap toward secure AI applications, agent security, evaluation, and responsible adversarial testing.
Current position
AI security is a direction I am building toward, not a claim of completed professional expertise.
Security of AI systems
The planned path includes secure AI application design, data and model trust boundaries, access control, monitoring, and resilient agent workflows.
Offense and defense
Future study will cover authorized adversarial testing and AI-enabled defensive automation with explicit scope, evidence, and human oversight.
Learning roadmap
Current cybersecurity foundations come first, followed by automation and application security, then deeper AI-security evaluation work.
Learning direction
A responsible AI-security pathway
This framework describes the areas Cody plans to deepen. It does not imply completed AI red-team work or production AI-security experience.
Governance
Define intended use, ownership, risk boundaries, and review criteria before testing.
Access and data
Limit privileges, protect sensitive inputs, and document how data enters the system.
Validation
Check outputs, failure modes, and misuse cases with repeatable, authorized tests.
Monitoring
Observe behavior, record relevant events, and create a response path for unexpected results.
A schematic showing that inputs, the model and data layer, and outputs all sit inside governance, access, validation, and monitoring controls.
Inputs
Users · data · tools
Model + application + data
Assets, behavior, integrations, and operating context
Outputs
Decisions · content · actions
Status: study direction. Any future project should document authorization, scope, test method, evidence, and limitations.