Privya.AI

Name: Privya.AI
Brand: Next.sec(AI)
Rating: 4.3 (6 reviews)

AI Native

Next.sec(AI) transforms how organizations secure AI systems by detecting hidden attack vectors. Formerly Privya.ai.

Est 2021|Raised $6M

LinkedIn 3K|Website|FTEs 12|HQ Tel Aviv, Tel-Aviv District, IL

MarketsData Privacy Management

01Competitive Landscape

Narrative analysis

Privya vs DSPM: Privya's approach focuses on code-level data-flow inference and claims to identify sharing and flow scenarios in code, while DSPM tools focus on production storage classification.
Privya vs runtime AI security: Privya analyzes artifacts rather than placing enforcement inline at runtime. This can reduce runtime overhead and operational complexity but may miss threats that manifest only during live interactions, such as some prompt-injection attacks or real-time exfiltration.
Market trend: AI-SPM and related terminology are being used by multiple vendors, indicating an evolving category with overlapping feature sets. Buyers should map needed capabilities (pre-production scanning, runtime enforcement, supply-chain intelligence) to vendor offerings.

- Privya vs DSPM: Privya's approach focuses on code-level data-flow inference and claims to identify sharing and flow scenarios in code, while DSPM tools focus on production storage classification. - Privya vs runtime AI security: Privya analyzes artifacts rather than placing enforcement inline at runtime. This can reduce runtime overhead and operational complexity but may miss threats that manifest only during live interactions, such as some prompt-injection attacks or real-time exfiltration. - Market trend: AI-SPM and related terminology are being used by multiple vendors, indicating an evolving category with overlapping feature sets. Buyers should map needed capabilities (pre-production scanning, runtime enforcement, supply-chain intelligence) to vendor offerings.

	Privya.AI	Lakera Guard	HiddenLayer AI Security Platform
Starting Price	Not publicly listed (demo/free scan available) — vendor engagement typically required	Pricing tiers public on some review platforms; exact tiers vary	Enterprise/contract pricing; marketplace listings indicate procurement-driven pricing
Primary Focus	Code-centric privacy and compliance scanning, and supply-chain visibility in the development lifecycle	Runtime LLM app protection focused on prompt injection and content filtering	Enterprise AI protections across MLOps pipeline with runtime visibility and AWS integrations
Key Strength	Detection of privacy-relevant entities and data-flow mapping in development artifacts; pre-production scanning approach	Runtime protection and inline enforcement for LLM apps	Enterprise integrations and runtime protections; procurement via cloud marketplaces
Key Weakness	Limited public documentation on integrations and benchmarks; small independent review volume	Focused on runtime use cases rather than pre-production privacy scanning	Enterprise complexity and sales-led pricing; public pricing not always transparent