Academic Project
A course final project building a triage application for suspected AI-generated (deepfake) audio and video evidence, exploring how investigators could systematically assess such evidence rather than relying on ad hoc review. The application performs SHA-256 hashing to preserve evidence integrity, extracts metadata, applies rule-based triage to identify indicators warranting further investigation, and produces structured forensic findings.
Outcome: Final grade: 95/100 (Digital Forensics course capstone project).
Professional ExperienceProfessional
Verification Specialist — Bill Gosling Outsourcing
Operational fraud, identity, privacy and compliance exposure: reviewed and validated customer applications, identity documents, and supporting records for compliance with financial-institution and privacy requirements; identified and escalated suspected fraud (forged identification, altered financial documents, impersonation attempts, account takeover indicators); supported identity and access-related checks for customer-facing systems; handled sensitive customer information against established procedural controls; and collaborated with fraud, compliance, and operations teams on suspicious-activity investigations, while maintaining record accuracy across high-volume, regulated case queues.
Outcome: 18,000+ customer applications, identity documents, and records reviewed and validated; 750+ suspected fraud attempts identified and escalated.
Commercial ProductPRE-LAUNCH
Independent, commercial SaaS product for service businesses, automating customer retention and recurring-service engagement: automated service reminders, customer follow-up, appointment/booking flows, recurring-customer engagement, and AI-assisted customer interactions. Amalachukwu is the primary product builder and owner. The product spans multiple application surfaces in a single monorepo/workspace architecture: a marketing/onboarding site, a customer application/dashboard, an admin surface, a booking flow, an embeddable widget, and an API.
Currently at production-readiness / pre-launch stage -- built and hardened as a real product, not yet claiming an established customer base.
Product-security work, approached from both the builder and AppSec sides: replacing demo/fake auth with real authentication (Supabase Authentication) including email verification and a secure sign-in/session flow; tenant/membership boundaries and role-based access control built on the principle of least privilege, enforced at the database layer with row-level/data-access controls rather than only in application code; removal of insecure client-side/admin shortcuts left over from earlier prototyping; secure environment-variable and secret handling, including application encryption-key handling; Stripe webhook signature verification; audit logging and structured, redacted application logging; permission-gated administrative actions; authentication regression testing; canonical production-domain/session consistency; onboarding entitlement and billing/trial-state validation; health/readiness endpoints; and a production-readiness security review before launch.
Outcome: A production-readiness security review covering authentication, tenant/access boundaries, secrets handling, webhook verification, and logging was completed ahead of launch -- treated as a pre-launch gate, not a post-incident fix.
Product in DevelopmentIN-DEVELOPMENT
AI Governance / Audit SaaS
A commercial platform intended to help organizations operationalize AI governance, with capability tiers described for small business (a plain-language assessment of AI-use safety plus the supporting documents), mid-market (AI inventory, risk assessments, policies, evidence, remediation), and enterprise (multi-team governance, approval workflows, control mapping, APIs, audit trails, RBAC, continuous monitoring). No customers, revenue, or production functionality are claimed -- this is stated explicitly as in development.