Mask LLM inputs: open-source PII detection & inline masking layer

Many organizations—especially software teams in healthcare, finance, legal, and large SaaS companies—are now routing natural-language inputs to hosted LLMs and risk accidentally exfiltrating PII or regulated data; this affects an estimated 2,000,000 businesses and plays into an $18.0B market where companies spend roughly $9,000 annually on privacy and data-protection tooling. The problem is operational: devs need pre-call, deterministic masking and audit trails that work at scale without adding much latency or breaking downstream model performance. You could build an open-source inline masking layer: a lightweight proxy/SDK that detects names, IDs, contact info, PHI, and contextual identifiers with hybrid rules+model detectors, applies configurable masking/tokenization policies, and emits immutable audit logs and redaction proofs. Offer easy integrations (Node/Python SDKs, middleware for common frameworks), on-prem and SaaS deployment options, and an enterprise tier for advanced analytics, policy management, and compliance SLAs. Timing favors this: LLM-API adoption is surging while regulators (GDPR, HIPAA, new regional laws like India’s) increasingly expect pre-call controls and auditable data flows, reflected in a high market score (95/100) and strong revenue potential (88/100). To stand out, emphasize open-source transparency and low-latency inline masking with pluggable detectors and verifiable audit trails so security teams can inspect and extend behavior, while monetizing through hosted services, enterprise features, and support. Real challenges include achieving high detection accuracy across languages and domains, minimizing false positives that impede functionality, and staying compatible with rapidly evolving LLM usage patterns—success will require a strong developer experience, rigorous benchmarking, and quick iteration rather than claims of solving PII perfectly.