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Orion Security

AI-driven, policy-free data loss prevention that analyzes data, systems, and business context instead of static rules.

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60/100Incremental Innovator

Overview

Orion Security is building what it calls “DLP beyond policies”: rather than relying on the manually authored, constantly maintained rule sets that make traditional data loss prevention brittle and noisy, its AI agents analyze the data itself, the systems it moves through, the people involved, and the surrounding business process to make contextual decisions about whether a data movement is actually risky.

Founded in 2024 by CEO Nitay Milner, a former product leader at Cisco-acquired Epsagon, and CTO Jonathan Kreiner, former application security lead at WalkMe, Orion is based in New York with an additional office in Tel Aviv. The company raised a $32 million Series A in February 2026 led by Norwest Venture Partners, with participation from IBM Ventures and existing investors PICO Venture Partners and Lama Partners. IBM published its own rationale for the strategic investment, a notable independent corporate-VC signal beyond the round itself.

Policy-free, contextual DLP is a meaningful rethink of one of security’s oldest and most maintenance-heavy categories, and the specific complaint it targets — brittle rules generating constant false positives — is real and well documented industry-wide. Several other data-security vendors are converging on similar AI-context approaches at the same time, though, and Orion hasn’t yet published named customers or independent test results, so its efficacy claims remain largely unproven in public.

Innovation Matrix Assessment

Innovation Velocity 6/10

Raised a $32M Series A with strategic backing from IBM Ventures within about two years of founding.

Operational Value 7/10

Directly targets the most common complaint about legacy DLP: brittle, high-maintenance policy rules that generate constant false positives.

Market Momentum 6/10

A $32M Series A with IBM Ventures as a strategic investor is a credible signal, though the company is still early-stage with no named enterprise customers found publicly.

Category Disruption 6/10

Policy-free, contextual DLP is a meaningful rethink of a very old category, though several DSPM/DLP vendors are converging on similar AI-context approaches simultaneously.

Real-World Efficacy 4/10

No independent testing, named customers, or case studies found yet; IBM's strategic backing signals confidence but is not efficacy proof.

Enduring Relevance 7/10

DLP for SaaS and AI-copilot data flows is an increasingly urgent enterprise need as sensitive data moves through more AI-assisted workflows.

Why CISOs Should Care

Aims to eliminate the constant policy-tuning burden of legacy DLP, using contextual analysis instead of static rules to cut false positives.

What Makes It Different

Analyzes the data, systems, people, and business process together rather than relying on manually authored DLP policy rules.

The Matrix Verdict

60/100 — INCREMENTAL INNOVATOR

A credibly backed rethink of a legacy category with strong strategic investor validation; Incremental Innovator pending public evidence of customer outcomes.

Editorial Note: Claims vs. Verified Findings

The Series A and IBM Ventures strategic investment are independently reported. Product-effectiveness claims are currently vendor-stated only; no named customers were found.

Sources