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The AI Security Evaluation Lab is a bounded research project for examining where AI can assist defensive work and where it should be constrained. Its focus is repeatable evaluation, adversarial inputs, traceability, and human accountability rather than autonomous decision-making.
Project signal
Human-in-loop
bounded assistance
Core stack
Python · OpenAI API · Evaluation Sets · Prompt Security
Architecture
Defines realistic analyst tasks, sensitive-data boundaries, and adversarial prompt-injection test cases.
Scores factual grounding, policy adherence, and useful escalation behavior against a controlled corpus.
Keeps a human accountable for decisions while recording model output, evidence links, and failure modes.
Capabilities
Operational views
These interface snapshots define the key evidence surfaces for the project. They are intentionally designed around investigation context rather than decorative dashboards.
View 01
Defines realistic analyst tasks, sensitive-data boundaries, and adversarial prompt-injection test cases.
View 02
Scores factual grounding, policy adherence, and useful escalation behavior against a controlled corpus.
View 03
Keeps a human accountable for decisions while recording model output, evidence links, and failure modes.
Roadmap
Continue exploring
SOC Engineering
A detection-first SOC operations workspace that brings telemetry health, triage context, and response playbooks into one deliberate workflow.
Home Lab
A repeatable Windows and Linux monitoring lab for testing telemetry, adversary behavior, and detections before production use.
Detection Engineering
A practical rule-development workflow that connects hypotheses, sample telemetry, test cases, and release decisions.