Personas
AI Developer Edition targets developers building AI-powered systems in regulated industries. These industries include financial services, healthcare, and public sectors who need to protect sensitive data across AI workflows. The primary persona is the Agentic AI Developer (Agent Builder).
Primary Persona: Agentic AI Developer (Agent Builder)
Agent builders create systems that go beyond chat/RAG. They plan, call tools, take actions, and coordinate with other agents. As agentic AI expands unstructured data use and introduces new pipelines, data protection complexity rises significantly.
| Attribute | Details |
|---|---|
| Role | Builds autonomous agent systems that plan, invoke tools, and coordinate across multi-agent architectures. |
| Pain Points | Sensitive data exposure in prompts/RAG/telemetry and across agentic workflows. Agents act with broader privileges than end users. Data crosses trust boundaries in multi-agent interactions. |
| Goals | Ship production-safe agents faster by embedding real-time PII protection directly into prompts, memory, and tool interactions without building custom privacy infrastructure. |
| Key Activities | Agent development, prompt/payload handling, retrieval pipelines, response rendering, telemetry/logging, tool calling using MCP, multi-agent orchestration using A2A. |
| Fit with AI Dev Edition | Strong Fit - mask/tokenize PII in prompts and data flows, semantic guardrails to prevent context poisoning, inline privacy for agent runtime. |
Value Proposition: Protegrity AI Developer Edition is the fastest way for agent builders to make LLM-powered systems safe for real data. This is achieved by embedding masking, tokenization, and semantic guardrails directly into agent workflows.
Without Protegrity, an agent builder must build: PII detection models or regex, masking/tokenization logic, audit/compliance layer, and governance rules. AI Developer Edition provides out-of-box APIs, a developer sandbox, and pre-built PII entity detection; accelerating dev-to-production and reducing attack surface, compliance risk, and security approval cycles.
Supporting Personas
The following personas have been considered when developing AI Developer Edition.
| Persona | Role Description | Pain Points | Fit with AI Developer Edition |
|---|---|---|---|
| Model Developer | Builds, trains, fine-tunes, and deploys AI models. Builds APIs and pipelines connecting LLMs to systems. | Training/data pipelines need tokenization/anonymization; sensitive data leakage in training data. | Strong Fit - Tokenization for training data, anonymization pipelines, synthetic data generation. |
| ML Engineer | Preps datasets for training/fine-tuning, manages feature stores and pipelines. Focuses on risk assessment, optimization, and data-driven decision-making. | PII minimization, consistent privacy across pipelines, governance, access controls, lineage. | Strong Fit - Tokenization for training data, consistent privacy across pipelines. |
| Prompt Engineer | Designs, tests, and optimizes prompts for generative AI models. Crafts precise instructions and evaluates outputs. | Context poisoning, sensitive information leakage to models and logs. | Medium Fit - Semantic guardrails for context poisoning prevention, data protection for leakage. |
| AI Application Developer | Integrates copilots with apps to automate processes. Embeds AI into enterprise services. | Connectors and admin-governed packaging for security needs. | Medium Fit - Protection APIs for copilot integrations. |
| Security Developer / Analyst | Part of security and risk teams focused on building security tools, defining policies, and implementing trust/risk/security management. | Information governance, runtime enforcement, audits, compliance. | Strong Fit - Discover and protect PII, policy simulation, audit capabilities. |
Feedback
Was this page helpful?