Riscosity

Name: Riscosity
Brand: Riscosity

AI Native

Riscosity safeguards organizations whose applications and users are innovating with any AI tool or service.

Est 2021|Valuation $19.7M|Raised $7M

LinkedIn 926|Website|Web traffic 1K|FTEs 26|HQ Austin, Texas, US

MarketsData Security Platforms

01Competitive Landscape

Narrative analysis

#### Where Riscosity appears differentiated - The vendor frames the product for AI-related data flows and enforcement. - Emphasis on third-party data flow cataloging and linkage to vendor risk management (VRM) workflows, such as ServiceNow integration. #### Where alternatives can be superior - Organizations with a broad SSE deployment (Netskope/Zscaler) may prefer consolidation into an existing vendor to reduce operational complexity. - For sensitive data stored across cloud repositories, DSPM or data governance vendors (Cyera/BigID) may provide more immediate, comprehensive discovery and classification for data-at-rest. #### Adoption / market visibility signals (limited) Riscosity has a listing on a major cloud marketplace (procurement channel visibility) and public reporting of funding and partnerships with third-party security firms, indicating go-to-market activity but not necessarily market leadership.

	Riscosity	Netskope (SSE/CASB/DLP)	BigID (Data Discovery/Governance)
Starting Price	Published procurement-tier pricing for smaller organizations (listed via cloud marketplace)	Generally quote-based; public procurement documents indicate premium positioning	Quote-based (contact vendor for pricing)
Primary Focus	Governing data in motion to third parties and AI vendors; shadow AI discovery; policy enforcement (block/redact/reroute)	Broad SSE stack (SWG/CASB/DLP), inline SaaS controls, web access control	Deep data discovery/classification across repositories; governance workflows (data at rest)
Key Strength	Focused on outbound data flow cataloging and AI-related enforcement controls	Mature platform breadth and ecosystem; wide enterprise adoption	Broad scanning/connectors and classification for stored data
Key Weakness	Limited public review footprint; potential overlap with existing SSE/DLP; routing/policy complexity	Admin complexity and deployment effort; premium pricing for broad deployments	Implementation complexity; does not natively enforce data-in-motion controls to AI vendors without additional controls

01Competitive Landscape

Narrative analysis

Where Riscosity appears differentiated

The vendor frames the product for AI-related data flows and enforcement.
Emphasis on third-party data flow cataloging and linkage to vendor risk management (VRM) workflows, such as ServiceNow integration.

Where alternatives can be superior

Organizations with a broad SSE deployment (Netskope/Zscaler) may prefer consolidation into an existing vendor to reduce operational complexity.
For sensitive data stored across cloud repositories, DSPM or data governance vendors (Cyera/BigID) may provide more immediate, comprehensive discovery and classification for data-at-rest.

Adoption / market visibility signals (limited)

Riscosity has a listing on a major cloud marketplace (procurement channel visibility) and public reporting of funding and partnerships with third-party security firms, indicating go-to-market activity but not necessarily market leadership.

	Riscosity	Netskope (SSE/CASB/DLP)	BigID (Data Discovery/Governance)
Starting Price	Published procurement-tier pricing for smaller organizations (listed via cloud marketplace)	Generally quote-based; public procurement documents indicate premium positioning	Quote-based (contact vendor for pricing)
Primary Focus	Governing data in motion to third parties and AI vendors; shadow AI discovery; policy enforcement (block/redact/reroute)	Broad SSE stack (SWG/CASB/DLP), inline SaaS controls, web access control	Deep data discovery/classification across repositories; governance workflows (data at rest)
Key Strength	Focused on outbound data flow cataloging and AI-related enforcement controls	Mature platform breadth and ecosystem; wide enterprise adoption	Broad scanning/connectors and classification for stored data
Key Weakness	Limited public review footprint; potential overlap with existing SSE/DLP; routing/policy complexity	Admin complexity and deployment effort; premium pricing for broad deployments	Implementation complexity; does not natively enforce data-in-motion controls to AI vendors without additional controls