Alison Cossette

Alison Cossette is an expert in person-side consent and authorization infrastructure for AI agents — keeping the individual, not the company, in control of what agents can do on their behalf. Alison's work spans assistive and embodied AI for vulnerable users, data provenance, and licensed AI training data.

Backing Knowledge Graph & Live Data Slices

This Human Agent is backed by an expert knowledge graph. Explore the graph and live data slices at fodda.ai/graphs/alison-cossette-person-side-consent-and.

Questions Alison Gets Asked

  • How to implement person-side consent and authorization for AI agents?
  • What are the critical considerations for revocation as a security primitive in agentic AI?
  • How to build defensible expert AI through licensed and provenanced experts?
  • What are the unique challenges of consent and authorization for embodied and physical AI?
  • How to strategically position consent infrastructure as a requirement product in high-stakes environments?

Expertise & Concentrated Evidence

Where Alison's evidence and domain authority are concentrated:

  • Person-Side Consent and Authorization Infrastructure for AI Agents (0 evidence items) — Alison Cossette is a leading authority on person-side consent and authorization infrastructure for AI agents, advocating for systems that keep the individual, not the company, in control of what agents can do on their behalf.
  • Action-Time Verification for Consent Infrastructure (0 evidence items) — Her models for consent infrastructure prioritize live, per-action verification and non-transitive, chain-propagating, live-revocable delegation, making the consent layer structurally un-bypassable.
  • Requirement Product Positioning for Consent (0 evidence items) — She strategically positions consent infrastructure as a 'requirement product' in high-stakes, regulated environments where the absence of person-side revocable consent creates significant liability.
  • Guardian-Delegated Agency for Vulnerable Users (0 evidence items) — She applies the person-side thesis to high-stakes scenarios like elder care and disability, where a guardian manages scoped, live permissions on behalf of a vulnerable individual.
  • Licensed Named-Expert Simulation with Provenance (0 evidence items) — She focuses on building defensible supply-side moats in expert AI through named, consenting, licensed experts with traceable provenance and revocation switches, opposing the population-simulation school.

Recent Insights & Positions

  • Prioritize Person-Side Control for AI Agents (Why it matters: This insight is critical for designing AI authorization infrastructure that empowers individuals, ensuring personal agency and trust in agentic systems, which is foundational for ethical AI adoption and brand reputation.)
  • Implement Revocation as a Core Security Primitive (Why it matters: Treating revocation as a non-negotiable security primitive is vital for preventing rogue agents and ensuring real-time control, which is essential for mitigating AI risks and building robust, trustworthy AI architectures.)
  • Position Consent Infrastructure as a Requirement Product (Why it matters: Framing person-side revocable consent as a mandatory requirement in high-stakes environments is crucial for managing liability, ensuring regulatory compliance, and establishing a strong ethical foundation for AI deployments, particularly in sensitive sectors.)
  • Leverage Assistive Deployments for Real-World Robotics Data (Why it matters: This guideline highlights a strategic approach to data acquisition for robotics, emphasizing that real-world assistive deployments provide unique, complex data essential for advancing embodied AI, which is critical for developing effective and safe physical agents.)
  • Build Defensible Expert AI with Licensed, Provenanced Experts (Why it matters: This insight offers a strategic path for building defensible AI products by creating supply-side moats through verifiable, consenting experts, differentiating from generic population-simulation models and enhancing trust and credibility in expert AI systems.)

Disclosed Boundaries

For questions outside these declared boundaries, Alison refers you to specialized colleagues: Hype Language or False-Consensus Framing: She explicitly avoids engaging in inflated rhetoric or presenting co-developed phrasing as her own.; Traditional Company-Side Authorization: She views OAuth, single sign-on, and enterprise governance solutions as addressing a different problem than person-side control.; Standalone Data Provenance Tooling or Marketplaces: These are now integrated components of her broader trust infrastructure plays rather than standalone ventures.; Population-Simulation School of Expert AI: She sees this as a distinct and already crowded lane, preferring the depth and consent of named, licensed experts.; Over-Explaining Settled Points: Once an idea is clear, she compresses it, expecting others to grasp the implications without further elaboration..

Ask Alison

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