Avalor: Funding, Team & Investors | Startup Intros

Financial History

Avalor has raised $30.0M across 2 funding rounds. Most recently, it raised $25.0M Series A in April 2023.

Total Raised

$30.0M

Valuation

N/A

Funding Rounds Raised

Date	Round	Lead Investors	Other Investors
Apr 1, 2023	$25.0M Series A		Battery Ventures, Kinnevik, Salesforce Ventures, TCV
Dec 1, 2021	$5.0M Seed		Cyberstarts VC, Greenfield Partners, Vintage Investment Partners

Date

Round

Lead Investors

Other Investors

Apr 1, 2023

$25.0M Series A

Battery Ventures, Kinnevik, Salesforce Ventures, TCV

Dec 1, 2021

$5.0M Seed

Cyberstarts VC, Greenfield Partners, Vintage Investment Partners

Deep Dive

Avalor is a cybersecurity technology company that built a data‑centric security platform (a “Data Fabric for Security”) to aggregate, correlate, and contextualize security data across tools and telemetry, and was acquired by Zscaler after rapid growth since its founding in late 2021[1][3].

High-Level Overview

Avalor’s platform organizes security assets (identities, vulnerabilities, controls, telemetry) into a unified data fabric so security teams can prioritize and remediate risk continuously rather than reactively[1][3]. The product is aimed at enterprise security teams and service providers that need to connect many security data sources and derive prioritized, actionable insights across those sources[1][3]. Growth momentum: Avalor was founded in December 2021, landed multiple Fortune 500 customers in a short period, and was acquired by Zscaler—an outcome positioned as an accelerant for faster customer reach and AI-enabled security insights[1][2][3].

Origin Story

Avalor was founded in December 2021 to address the practical problem that organizations lacked an effective way to organize and extract deep context from disparate security data sources[3]. The founding team came from a data/engineering background (described by Avalor as “data people who hadn’t built security products before”), and they designed a flexible, extensible data model to correlate security information across many third‑party sources[1]. Early traction included rapid adoption by several large enterprise customers and attention from Zscaler, which led to Avalor’s acquisition and the promise to operate the Avalor team independently within Zscaler to scale their Data Fabric for Security™ capabilities[1][2].

Core Differentiators

Data‑first architecture: Avalor’s core is a flexible, extensible *Data Fabric for Security* that aggregates assets, controls, vulnerabilities, identities and telemetry into a correlated model rather than shipping a single point product[1][3].
Breadth of integrations: Prior to acquisition Avalor already supported 150+ third‑party data sources, enabling broad contextualization across toolchains and telemetry[1].
Focus on continuous risk management: The product emphasizes continuous, prioritized risk discovery and remediation (attack‑path/kill‑chain analysis, vulnerability prioritization) instead of one‑off scans[1].
AI and analytics enablement: The platform’s data model is positioned to accelerate advanced analytics and AI use cases (threat prioritization, automated remediation) when combined with a large telemetry footprint like Zscaler’s[1].
Rapid enterprise traction and M&A validation: Fast customer adoption and the acquisition by Zscaler signal product‑market fit and strategic value to a major security vendor[1][2].

Role in the Broader Tech Landscape

Trend alignment: Avalor rides the consolidation/centralization trend in security where organizations seek unified observability and risk‑prioritization across an expanding, fragmented tooling landscape[1][3].
Timing: As enterprises generate massive volumes of security telemetry and vendors push AI for security insights, a robust data fabric that unifies and contextualizes that data is timely and valuable[1].
Market forces: Growing attack surfaces, regulatory pressure for better risk governance, and the proliferation of point security tools make centralized correlation and prioritization increasingly necessary[1].
Influence: By enabling richer cross‑tool context and AI workflows, Avalor’s approach helps shift the industry from isolated alerts toward continuous threat exposure management and automated remediation—especially when scaled through Zscaler’s global telemetry and customer base[1].

Quick Take & Future Outlook

What’s next: Operating inside Zscaler, Avalor’s data fabric is likely to be scaled across Zscaler’s 7,000 customers and integrated with Zscaler telemetry to power advanced AI‑driven security insights (automated prioritization, attack‑path analysis, remediation orchestration)[1]. Trends that will shape the journey include continued demand for centralized risk orchestration, expansion of CTEM (continuous threat exposure management) use cases, and increased regulator/customer expectations for demonstrable risk reduction[1]. Potential evolution: Avalor’s technology could become a foundational data layer for Zscaler’s analytics and AI products, accelerating adoption of automated security operations and changing how enterprises measure and reduce exposure over time[1].

If you’d like, I can:

Summarize Avalor’s product capabilities in a one‑page technical brief.
Map the 150+ integrations Avalor supported to common security use cases (vulnerability management, identity risk, telemetry correlation).
Draft a short slide deck outlining the strategic rationale for Zscaler’s acquisition based on public statements and product fit.

Deep Dive

High-Level Overview

Origin Story

Core Differentiators

Data‑first architecture: Avalor’s core is a flexible, extensible *Data Fabric for Security* that aggregates assets, controls, vulnerabilities, identities and telemetry into a correlated model rather than shipping a single point product[1][3].
Breadth of integrations: Prior to acquisition Avalor already supported 150+ third‑party data sources, enabling broad contextualization across toolchains and telemetry[1].
Focus on continuous risk management: The product emphasizes continuous, prioritized risk discovery and remediation (attack‑path/kill‑chain analysis, vulnerability prioritization) instead of one‑off scans[1].
AI and analytics enablement: The platform’s data model is positioned to accelerate advanced analytics and AI use cases (threat prioritization, automated remediation) when combined with a large telemetry footprint like Zscaler’s[1].
Rapid enterprise traction and M&A validation: Fast customer adoption and the acquisition by Zscaler signal product‑market fit and strategic value to a major security vendor[1][2].

Role in the Broader Tech Landscape

Trend alignment: Avalor rides the consolidation/centralization trend in security where organizations seek unified observability and risk‑prioritization across an expanding, fragmented tooling landscape[1][3].
Timing: As enterprises generate massive volumes of security telemetry and vendors push AI for security insights, a robust data fabric that unifies and contextualizes that data is timely and valuable[1].
Market forces: Growing attack surfaces, regulatory pressure for better risk governance, and the proliferation of point security tools make centralized correlation and prioritization increasingly necessary[1].
Influence: By enabling richer cross‑tool context and AI workflows, Avalor’s approach helps shift the industry from isolated alerts toward continuous threat exposure management and automated remediation—especially when scaled through Zscaler’s global telemetry and customer base[1].

Quick Take & Future Outlook

If you’d like, I can:

Summarize Avalor’s product capabilities in a one‑page technical brief.
Map the 150+ integrations Avalor supported to common security use cases (vulnerability management, identity risk, telemetry correlation).
Draft a short slide deck outlining the strategic rationale for Zscaler’s acquisition based on public statements and product fit.

Avalor

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