Scope before action
The work starts by defining what will be reviewed, what will not be touched, what access is needed, and what deliverables are expected.
Security Approach
Security guidance should be careful, authorized, and usable
The approach is practical: reduce avoidable risk, avoid unnecessary data exposure, explain tradeoffs clearly, and document what was agreed before work begins.
Authorization-first work
Security review and testing only happen with documented permission and only against systems the client owns or is authorized to assess.
Minimal data collection
Engagements are planned to avoid collecting unnecessary data. The launch website does not collect or store inquiry data.
Plain-English risk reporting
Findings are written for business decisions: what matters, why it matters, and what to do next.
Practical controls
Recommendations emphasize MFA, password managers, domain/email hygiene, backups, vendor settings, policy, and workflow controls.
AI-specific risks
AI work considers sensitive prompts, vendor settings, prompt injection, data leakage, employee misuse, tool permissions, AI-generated code risk, hallucination, verification, and shadow AI.
Small-business affordability
The goal is right-sized guidance and secure lightweight tooling, not enterprise complexity for its own sake.
Documentation and handoff
Engagements should leave the business with usable notes, checklists, policies, roadmap items, or tool documentation.
AI risk topics
AI safety is partly a security problem and partly a workflow problem
Sensitive data in prompts
AI vendor settings
Prompt injection
Data leakage
Employee misuse
Over-permissioned tools
AI-generated code risks
Hallucination and verification
Shadow AI usage
Not sure where to start?
Have an AI workflow that needs a second look?
Threat modeling, vendor review, policy, and secure workflow design can catch practical issues before adoption spreads.