ORION Security Secures $32M to Redefine Data Loss Prevention for the AI Era

ORION Security has closed a $32 million Series A funding round, marking a significant step forward for a company aiming to redefine how enterprises protect sensitive data. The round was led by Norwest, with participation from IBM and existing investors including PICO Venture Partners and Lama Partners. The raise brings ORION’s total funding to $38 million less than a year after its seed round, signaling strong early momentum and growing demand for a new approach to data loss prevention.

ORION is positioning itself as a modern alternative to traditional data loss prevention tools, which many security teams view as outdated, noisy, and difficult to manage. As organizations struggle with sprawling SaaS usage, remote work, and AI-powered workflows, the company argues that policy-based DLP has reached its limits.

Why Traditional Datal Loss Prevention Is Failing Enterprises

For nearly two decades, Data Loss Prevention (DLP) products have relied on manually written policies designed to block or flag predefined behaviors. In practice, these systems generate massive volumes of false positives and require constant tuning by security teams. Even with heavy maintenance, they often fail to detect real data exfiltration because they are limited to known patterns and static rules.

This problem has grown more acute as enterprise environments have become more dynamic. Employees now move sensitive information across cloud apps, collaboration tools, APIs, and AI systems in ways that are difficult to model with rigid policies. As a result, security teams are forced to choose between locking systems down so tightly that productivity suffers or loosening controls and accepting increased risk.

ORION was built around the idea that the core flaw in DLP is not poor execution but the policy model itself.

A Context-Driven, Autonomous Approach to Data Protection

Rather than relying on predefined rules, ORION uses specialized AI agents to understand the full context behind how and why data is moving. The platform evaluates factors such as data sensitivity, user identity, behavioral patterns, data lineage, and environmental intent in real time.

This contextual understanding allows ORION to determine whether an action represents a legitimate workflow or a genuine risk. Instead of flooding teams with alerts that require manual review, the system is designed to autonomously prevent harmful data exfiltration before it happens while allowing normal business activity to continue uninterrupted.

By focusing on intent and context rather than static rules, ORION aims to dramatically reduce false positives while identifying incidents that legacy DLP tools routinely miss. The company describes this as a shift from reactive enforcement to proactive prevention.

Early Traction in High-Stakes Industries

According to the company, ORION is already being used by enterprise customers in sectors such as finance, healthcare, and technology. These industries face intense regulatory pressure and handle large volumes of highly sensitive data, making them early adopters of more advanced security models.

The new funding will be used to accelerate development of ORION’s proprietary end-to-end architecture, expand its team of AI and security engineers, and scale go-to-market efforts to meet growing enterprise demand. Strategic participation from IBM also suggests potential future integrations and enterprise partnerships.

Norwest partner Dave Zilberman described ORION as redefining how enterprises safeguard data, emphasizing that autonomous, context-driven protection represents a structural shift rather than an incremental improvement.

Built by Security and Product Veterans

ORION was founded in 2024 by CEO Nitay Milner and CTO Jonathan Kreiner. Milner previously served as a product leader at Epsagon, which was acquired by Cisco, while Kreiner led application security initiatives at WalkMe. Their combined backgrounds in observability, application security, and enterprise software helped shape ORION’s focus on automation, accuracy, and operational simplicity.

From the outset, the company set out to eliminate the need for constant policy tuning and human intervention. That design philosophy is reflected in a platform intended to operate continuously in the background, adapting to evolving behaviors without requiring security teams to act as full-time rule writers.

The Broader Implications for Data Security

ORION’s approach reflects a wider shift taking place across enterprise security as data becomes more fluid and harder to define. Sensitive information now moves constantly across cloud platforms, collaboration tools, APIs, and AI systems, often in ways that cannot be predicted in advance. In this environment, security models built around static rules and predefined assumptions are increasingly strained.

As AI becomes embedded in everyday workflows, data protection is moving toward systems that can interpret context and intent rather than simply enforce policies. Instead of trying to specify every acceptable action ahead of time, future security platforms are likely to focus on learning normal behavior and intervening when activity meaningfully deviates from it. This mirrors earlier transitions in cybersecurity, where behavior-based detection replaced rigid, signature-driven approaches.

The broader implication is a shift in how security teams operate. Less time is spent maintaining rules and chasing alerts, and more time is spent overseeing automated systems and managing risk at a higher level. As this model becomes more common, data loss prevention may evolve from a reactive control into a continuous, adaptive layer that better aligns with how modern organizations actually work.