Brief
Privacy engineer Modupe Akintan calls for AI systems to embed digital confidence
Modupe Akintan argues that digital confidence must be engineered through privacy controls, not marketed as a feeling.
By Felo News Desk · Published
Modupe Akintan, a privacy and AI engineer, says digital confidence depends on whether people believe that AI systems have been designed with privacy risks in mind, rather than on the amount of data they collect. In an interview with Digital Journal, she describes privacy engineering for AI as a discipline that builds technical controls, risk assessments and standards into code, contracts and workflows before systems move from prototype to production.
What happened
Akintan outlines her career path, noting a first‑class computer engineering degree from Afe Babalola University in Nigeria, a master’s in computer science with a focus on security from Stanford, and research work in Stanford’s Empirical Security Research Lab that examined third‑party risk‑management models. She now works at the intersection of engineering, governance and policy, aiming to translate legal and policy requirements into enforceable technical constraints for AI, including autonomous agents.
What the reports add
Digital Journal reports that regulators are warning AI can only move as fast as trust allows, and industry white papers are coining the term “confidence advantages” to describe competitive edges gained through robust governance. The outlet notes that fragmented AI and privacy laws are prompting principle‑based governance to keep products compliant, and that confidence is achieved when ethics and reliable controls enable speed without danger.
What was said
"In this moment, digital confidence depends less on how much data a system can collect and more on whether people believe that, when it touches their lives, someone has actually thought through the risks to their rights," Akintan told Digital Journal.
She added, "If you have to ask users for blind trust, you haven’t done the work yet. Digital confidence should be the by‑product of good governance and careful design, not a marketing objective in its own right."
How it came about
Akintan’s focus on privacy engineering grew from academic research on risk‑rating methodologies for third‑party vendors, which highlighted the gap between risk models and the practical visibility of those risks for users. Her work now seeks to embed accountability pathways into AI systems so that harms can be traced and mitigated, turning abstract legal obligations into concrete technical safeguards.
Key facts
- Modupe Akintan defines digital confidence as trust earned through engineered privacy controls, not marketing. (digitaljournal.com)
- She holds a first‑class computer engineering degree from Afe Babalola University and a master's in computer science from Stanford. (digitaljournal.com)
- Privacy engineering for AI designs technical controls, risk assessments and standards into code before production. (digitaljournal.com)
Sources
- [1] digitaljournal.com — originally reported as “Architect of digital confidence: Why privacy engineering for AI systems is becoming its own discipline”
