Enterprise data does not move the way it used to. A few years ago, security teams could focus on email attachments, file downloads, USB drives, and a handful of cloud apps. Today, sensitive information moves through browsers, SaaS platforms, collaboration tools, AI assistants, prompts, code editors, documents, and automated workflows. The old idea of a clean security perimeter no longer fits how modern teams work.
That is the problem Nitay Milner is trying to solve with ORION Security.
As the Co-Founder and CEO of ORION Security, Nitay Milner is building a company focused on one of the most frustrating problems in enterprise cybersecurity: how to stop sensitive data from leaking without drowning security teams in rules, false alerts, and manual work. His company is not trying to add another layer of heavy policy management. Instead, ORION Security is taking a more modern path by using AI, context, and real-time understanding to protect data as it moves.
The timing matters. Generative AI has changed how employees create, analyze, and share information. It has also opened new ways for company data to leave controlled environments. A sales team may paste customer notes into an AI tool. A developer may use code assistance with proprietary source code. A finance employee may upload sensitive files into an unmanaged app for quick analysis. In many cases, the intent is not malicious. But the risk is still real.
This is where Nitay Milner’s work with ORION Security becomes important. The company is part of a wider shift in cybersecurity, where tools need to understand not only what data is moving, but why it is moving, who is moving it, where it is going, and whether the action makes sense for the business.
Who is Nitay Milner
Nitay Milner is best known as the Co-Founder and CEO of ORION Security, a data security startup built for the AI era. Before launching ORION Security, he worked in product leadership at Epsagon, a cloud observability company that was later acquired by Cisco. That background matters because observability and data security are more connected than they may seem at first.
In observability, teams try to understand what is happening across complex systems. They track behavior, patterns, signals, anomalies, and context. In data security, especially modern data loss prevention, the challenge is similar. Security teams need to understand how sensitive information behaves across the business and when that behavior becomes risky.
That product mindset appears to shape Nitay Milner’s approach at ORION Security. The company is not only focused on detection. It is focused on reducing friction for security teams and making data protection easier to operate. This is important because many legacy DLP systems became known for being difficult to manage. They often required large numbers of manual policies, constant tuning, and repeated investigation of alerts that turned out to be harmless.
By building ORION Security, Nitay Milner is aiming at a painful gap in the enterprise security market. Companies need stronger data protection, but they cannot afford security tools that slow down daily work or overwhelm already stretched teams.
What ORION Security is building
ORION Security is building an AI-powered data loss prevention platform. In simple terms, the company wants to help organizations stop sensitive data from leaving the business in risky or unauthorized ways.
Traditional data loss prevention tools usually depend on policies. A company writes rules about what should be blocked, monitored, or allowed. For example, a rule may stop credit card numbers from being sent outside the company. Another rule may block certain file types from being uploaded to personal cloud storage. This model can work in predictable cases, but it becomes harder to manage when the business changes quickly.
ORION Security is taking a different approach. The company uses AI to look at the wider context around data movement. That means the platform can consider signals such as content sensitivity, user identity, business context, destination, data lineage, behavioral intent, and the environment where the action is happening.
This is the core idea behind ORION Security’s pitch: data protection should not depend only on static rules. It should understand the real situation.
For example, sending a customer list to an approved internal analytics tool may be a normal business action. Sending the same customer list to an unknown personal account or an unmanaged AI application may be risky. The data may be the same, but the context is completely different. ORION Security wants to help security teams make that distinction in real time.
Why traditional DLP is under pressure
Legacy DLP tools were built for a different era of work. They were useful when data movement was easier to define and the number of channels was more limited. But the modern enterprise is far more complicated.
Employees now work across cloud apps, SaaS tools, remote devices, browser sessions, file-sharing platforms, and AI products. Sensitive data can move in small pieces, through copy and paste, screenshots, prompts, exports, API connections, and automated agents. A system that only relies on fixed rules can miss these changing patterns or block legitimate work by mistake.
This is why many security teams struggle with false positives. A false positive happens when a security system flags something as risky even though it is normal or harmless. Too many false positives create alert fatigue. When teams spend their time reviewing noise, they have less time to focus on real threats.
Policy sprawl is another issue. As a company grows, its DLP policies often multiply. Teams add rules for new apps, new departments, new compliance requirements, new file types, and new business workflows. Over time, the system can become hard to maintain. Rules may overlap, become outdated, or fail to reflect how employees actually work.
Nitay Milner is building ORION Security around the belief that better policies alone are not enough. The company’s approach is based on the idea that data security needs contextual intelligence, not just bigger rulebooks.
How Nitay Milner is changing the DLP conversation
The strongest part of Nitay Milner’s work is the shift in thinking. Instead of treating DLP as a policy management problem, ORION Security treats it as a context problem.
That may sound like a small difference, but it changes the whole product direction.
A policy-heavy tool asks, “Does this action match a rule?” A context-aware system asks, “Does this action make sense?”
That second question is far more useful in the AI era. Data risk is not always obvious from the content alone. The same file can be safe in one workflow and dangerous in another. The same user can perform a normal task in the morning and a suspicious one later if the destination, timing, or behavior changes.
ORION Security uses AI agents and large language models to analyze these signals more deeply. The goal is to understand data movement the way a skilled security analyst might, but at machine speed and enterprise scale.
This is where Nitay Milner’s product background becomes valuable. The challenge is not simply building a powerful security engine. The real challenge is making that engine useful, reliable, and practical for companies with thousands of employees and constant data movement.
ORION Security and AI-era data risk
The rise of generative AI has made data loss prevention more urgent. Employees use AI tools because they help them move faster. They can summarize documents, rewrite messages, analyze spreadsheets, draft code, create reports, and generate ideas. But every prompt, file upload, or copied text block can carry sensitive data.
This creates a new type of security problem. Many data leaks may not come from attackers. They may come from well-meaning employees using helpful tools without realizing where the data is going.
For businesses, that risk can include customer records, financial data, source code, contracts, unreleased product plans, health data, payment information, intellectual property, or internal strategy documents. Once this data enters an unmanaged third-party AI tool, the organization may lose control over it.
ORION Security is being built for this reality. Its approach looks beyond old channels and focuses on how data actually moves through modern work. That includes humans, SaaS apps, browsers, cloud environments, and eventually AI agents acting on behalf of employees.
This is one of the reasons Nitay Milner’s company has attracted attention. The market is not only asking for better DLP. It is asking for a new kind of data security that can keep up with AI adoption.
The funding milestone that raised ORION Security’s profile
ORION Security gained wider attention after closing a $32 million funding round led by Norwest, with participation from IBM and existing investors including PICO Venture Partners, Lama Partners, Underscore VC, and others. The round brought the company’s total funding to $38 million.
For a young cybersecurity company, that level of backing is meaningful. It suggests that investors see a real market need for AI-native data protection. It also gives ORION Security more room to grow its product, invest in research and development, and expand its go-to-market work.
Funding alone does not prove a company will win, but it does show momentum. In ORION Security’s case, the funding story lines up with a clear industry shift. Companies are under pressure to adopt AI, protect sensitive data, meet compliance demands, and reduce operational strain on security teams. A platform that can address these needs in a more automated way has a strong reason to exist.
For Nitay Milner, this milestone also strengthens his position as a founder working on a high-value problem at the right moment. His success is not only about raising money. It is about identifying a broken category and building a company around a more practical answer.
Why ORION Security matters for enterprise teams
Enterprise security teams are often asked to do two things that pull in opposite directions. They need to protect the company from data leaks, but they also need to avoid slowing down employees. If controls are too weak, sensitive data can escape. If controls are too aggressive, employees look for workarounds or complain that security is blocking productivity.
This is why context matters so much.
A smarter DLP platform can help security teams focus on the actions that truly look risky. It can reduce unnecessary alerts, lower the amount of manual tuning, and give analysts better explanations for why something was blocked or allowed.
ORION Security is trying to make data protection feel less like a rigid gate and more like an intelligent layer around the business. That matters for industries such as finance, healthcare, technology, legal services, and any organization handling regulated or confidential information.
The promise is simple but powerful: protect sensitive data without turning security into a daily obstacle.
Nitay Milner’s founder advantage
One reason Nitay Milner’s story is interesting is that he is not approaching data security as a distant market trend. His background in product and observability gives him a practical lens on how enterprise software succeeds or fails.
Security products do not win only because they are technically advanced. They win when teams can actually use them, trust them, and fit them into real workflows. A tool that creates too much noise becomes a burden. A tool that blocks too much work becomes unpopular. A tool that requires endless tuning becomes expensive to operate.
ORION Security appears to be built around these lessons. Its focus on real-time context, AI agents, and reduced policy dependence reflects a founder who understands that enterprise teams want better outcomes, not more dashboards and rules to manage.
This is where Nitay Milner’s achievement stands out. He is building in a crowded cybersecurity market, but he is aiming at a problem many companies already understand from experience. If ORION Security can make DLP more accurate, more adaptive, and less painful, it can become part of a much bigger shift in how companies secure their data.
How ORION Security fits into the future of data protection
The future of data security will not be defined by one channel or one type of leak. Data will keep moving across people, apps, cloud platforms, AI copilots, automated workflows, and machine-driven agents. Security tools will need to understand that movement in real time.
That is the future Nitay Milner is building toward with ORION Security.
The company’s work points to a broader change in cybersecurity. Enterprises are moving away from tools that only react to known patterns and toward systems that can reason about context. In the AI era, that shift is not optional. Data moves too quickly, and the risk patterns change too often.
ORION Security is positioning itself as a modern answer to a long-standing problem. Instead of asking security teams to keep writing more policies, the company wants to give them a system that understands data movement more naturally. That approach could make data protection stronger, faster, and easier to manage.
For Nitay Milner, the achievement is not just launching another security startup. It is building a company around a real pain point at a moment when enterprises are rethinking how they protect their most valuable information.