Bold vs. Cyberhaven: Endpoint DLP Comparison

Bold vs. Cyberhaven: key takeaways

Bold and Cyberhaven both sell data loss prevention software for managed endpoints, and both were built to replace legacy DLP products that depend on regular expressions, keyword dictionaries, and manually written policy rules. They disagree on where the detection engine should run and what it should read.

• Where detection runs: Cyberhaven installs an endpoint agent, a browser extension, and cloud API connectors into sanctioned SaaS applications, then performs the analysis in its own cloud. Bold runs small language models on the endpoint itself, so classification and enforcement happen in the same place as the file, the clipboard action, or the AI prompt.
• What classification reads: Cyberhaven classifies primarily by data lineage, meaning it infers sensitivity from where data originated, such as a Salesforce export or a GitHub repository, supplemented by content identifiers for PII, PCI, and PHI. Bold classifies by content meaning across four dimensions, so source code, financial models, and board material are recognized without a rule or a known origin.
• Offline and untrusted networks: Bold classifies and blocks with no network connection. Cyberhaven's documentation describes its endpoint agent performing processing in the cloud, so offline enforcement is a question to test in a POV.
• Data privacy and residency: With Bold, no file content leaves the device for analysis, encrypted evidence lands in your own cloud tenant under your key, and Bold receives metadata that can be masked. Cyberhaven analyzes in its cloud and offers optional screenshot and file capture.
• Coverage each one wins: Cyberhaven also sells DSPM for data at rest across SaaS, PaaS, and IaaS, which Bold does not. Bold covers local activity that never crosses the network, including clipboard, USB, AirDrop, printing, and desktop applications that encrypt their own traffic.

Use cases covered in this comparison: endpoint data loss prevention, insider risk management, and AI usage control, including data moving into chatbots, AI browsers, and local coding assistants.

Cyberhaven background

Cyberhaven was founded in 2016 and is headquartered in San Jose, California. Its founders are Volodymyr Kuznetsov, Cristian Zamfir, George Candea, Radu Banabic, and Vitaly Chipounov, a group of computer scientists out of EPFL and MIT whose work began as a research effort that won a DARPA Cybersecurity Challenge. The company has raised roughly $236 million across five rounds, most recently a $100 million Series D in April 2025 led by StepStone Group with Schroders and Industry Ventures, at a $1 billion valuation.

Cyberhaven is no longer founder-led. Howard Ting ran the company from 2020 until May 2025, when he moved to the board and Nishant Doshi, who had joined in 2024 as chief product and development officer, stepped in as interim and then permanent CEO. Doshi founded CirroSecure, acquired by Palo Alto Networks, and Propelo, acquired by Harness. Co-founder Volodymyr Kuznetsov, who was previously CEO, is now chief technology officer. In October 2026 the company added a go-to-market leadership layer, naming Todd Rotger president of go-to-market alongside a new chief product officer, chief marketing officer, and SVP of customer experience.

The product has moved as fast as the org chart. In February 2026, Cyberhaven announced general availability of a unified platform combining DSPM, data loss prevention, insider risk management, and AI security in a single architecture, and the company reported triple-digit annual growth for its fiscal year. Its current positioning centers on what it calls the agentic enterprise, delivered through a product called Cyberhaven Flow. Its homepage headline reads "AI Changed Work. We Protect It." On its product pages, Cyberhaven calls itself "the original data lineage company" and describes lineage as "a technology that's only available from Cyberhaven."

That claim is worth taking seriously. Lineage is a real engineering achievement, Cyberhaven has close to a decade of work behind it, and the company has customers in frontier AI labs, banks, and law firms who say in their own words that it solved problems legacy DLP could not.

Why companies are replacing DLP

Data loss prevention is not a new category. Symantec, Forcepoint, McAfee, and Microsoft have sold DLP for close to two decades, and most enterprises already own at least one of those products. The reason comparisons like this one exist is that the assumptions those products were built on no longer describe how work happens.

Pattern matching only finds data that follows a pattern

Legacy DLP detects sensitive data with regular expressions, keyword dictionaries, and exact data matching. That works for credit card numbers, Social Security numbers, and medical record identifiers, because those have a fixed shape. It does not work for the data most companies would be hurt by losing: source code, model weights, pricing models, a customer list in a spreadsheet with no header row, pre-release product designs, M&A documents, and board material. None of that has a pattern to write. The result is a DLP deployment that blocks harmless strings of digits and misses the design file that walks out on a USB stick.

The endpoint is where sensitive data is created, combined, and lost

Network and cloud controls inspect traffic in transit and data at rest inside sanctioned applications. A laptop is neither. It is where an employee exports a report from Salesforce, pastes rows into a local spreadsheet, renames the file, compresses it, and drops it into a personal cloud folder or a chat window. Most of those steps never cross a proxy and never touch a SaaS API. Endpoint data loss prevention exists because the device is the one place with visibility into the full sequence.

AI moved the exfiltration path off the network

The fastest-growing data loss path is no longer an email attachment. It is a prompt. Employees paste customer records into consumer chatbots, use AI browsers that read whatever is on screen, and run local coding assistants that read entire repositories before calling an external model. Much of that activity is encrypted by the application itself, or happens on the device before anything is transmitted. Controls that depend on inspecting network traffic see an encrypted tunnel and nothing more.

What buyers evaluate when they replace a DLP product

Five criteria usually decide these evaluations: whether the product classifies proprietary data without a rule written for it, whether it enforces in real time rather than alerting after the fact, whether it keeps working offline and on untrusted networks, how much sensitive content has to leave the company to be analyzed, and how many weeks of tuning stand between purchase and useful coverage.

The three use cases in this comparison

Endpoint data loss prevention: stopping sensitive data from leaving a managed device through web uploads, email, personal cloud accounts, removable media, printing, clipboard, and screen capture, at the moment of the action rather than in a report the next morning.

Insider risk management: identifying when an employee or contractor, particularly one who has resigned or changed roles, is collecting, obfuscating, or moving data they have access to but no business reason to take, and producing evidence a Legal or HR team can act on.

AI usage control: seeing which AI tools, AI browsers, and coding assistants are running on each device, what data is going into them, and enforcing policy on that data without blocking the tools employees need to do their jobs.

Bold vs. Cyberhaven: Key differences

The decision is made on the device

Bold runs small language models locally, using the CPU and memory already in the laptop. A file opened in Excel, a block of source code copied out of an IDE, a prompt typed into a desktop AI application, and a document dragged to a USB drive are all classified and ruled on in the same place they occur. There is no round trip to a detection cloud, no per-event inference cost, and no dependency on connectivity for the verdict to arrive before the action completes.

Cyberhaven's endpoint agent is documented as collecting on the device and performing processing in its cloud, which is a reasonable trade for device performance and is how most of the DLP category works, including Netskope, Forcepoint, and Microsoft Purview's endpoint components. It also means the decision about whether a given upload is customer PII arrives from a service outside the device.

Classification reads the content, not the path it took

Lineage answers the question of where data came from. Bold answers what the data is. Bold's models classify any data type by meaning across four dimensions at once: sensitive elements, business artifact, content category, and data affiliation. Source code, financial models, board material, and proprietary formats are classified on day one with no pattern written for them.

This matters when lineage is thin or absent, which on a working laptop is often. A quarterly forecast built from scratch in Excel, a contract received through personal email, a design exported from an unmanaged tool, an architecture document generated by a chatbot, and a repository cloned from a system with no API connector all arrive with no origin story to trace. Bold classifies those on what they contain. Cyberhaven supplements lineage with out-of-the-box content identifiers for PII, PCI, and PHI plus customer-written regular expressions, which is the same pattern-matching method that leaves proprietary data uncovered in legacy DLP.

Enforcement holds offline and inside encrypted apps

Disconnect a Bold-protected laptop from the network and it keeps classifying and enforcing. The same is true on untrusted networks, inside desktop applications that encrypt their own traffic, and for local activity that never crosses the wire: files moving between folders, clipboard activity, USB, AirDrop, and printing.

This is the clearest architectural split between the two products and the easiest to test. Take a laptop off the network, open a sensitive engineering document, copy a section into a personal webmail draft, and try to write the original to a USB drive. Then repeat the test on a hotel network with a captive portal. Whatever each product does in those two minutes is the answer.

Analysis happens on the device, so file content stays where it is

Because classification runs locally, no file content, prompt text, or screen content is transmitted to Bold or to any third-party model for analysis. Encrypted evidence is written to your own cloud tenant under your own encryption key. Bold receives metadata only, and that metadata can be masked. Monitoring is scoped by data provenance, so a personal tax return moving from a personal account to a personal cloud folder can be left unmonitored, while a customer database export is watched.

For banks, hospitals, defense suppliers, and any company with a works council or a data residency obligation under GDPR, that architecture changes the procurement conversation and not only the technical one. Cyberhaven's model, with cloud-side analysis and optional capture of screenshots and the files themselves, answers those same questions differently and will be reviewed on those terms.

Day one coverage without a configuration project

Bold's models are pre-trained and classify from the moment the software lands on the endpoint. There is no rule library to build, no two-week baseline period before enforcement can be turned on, and no blank policy screen asking which data types matter to you. Teams that need something no vendor could have anticipated, such as a proprietary CAD format or an internal document template, build a custom classifier from a handful of examples.

A person and a process are not the same actor

Bold uses process-tree analysis on the device to tell a human action from one taken by local software, including coding assistants and background processes operating on their own. This is evidence that on-device context is richer, not a claim that no one else can see agent activity. Cyberhaven markets agent coverage directly, including governance of coding agents like Claude Code and Cursor, and any evaluation should test both.

Bold vs. Cyberhaven comparison

Bold channel and operating system coverage

Comparison tables are easy to write and hard to verify, so here is Bold's coverage by channel and operating system, including the channels where Bold monitors today rather than blocks. Ask any vendor you are evaluating, including us, for the same table.

Key Cyberhaven features

Cyberhaven sells a single product with four modules fed by the same lineage engine: data loss prevention, insider risk management, DSPM, and AI security. The features below are described as Cyberhaven documents them on its own site, in the order a team evaluating endpoint data loss prevention usually runs into them.

Data lineage

Cyberhaven records events for each piece of data, including copies, edits, format conversions, compression, renames, and uploads, and keeps the chain intact as the data changes shape. It uses that history to infer what the data is based on where it started, such as a Salesforce export or a GitHub repository.

Flow and Linea AI

Cyberhaven Flow is the company's current product framing, described as an AI-native experience powered by data lineage and a set of agents: configuration, classification, detection, and analyst agents. Linea AI is the analysis layer that reads lineage and summarizes incidents for reviewers.

Modern DLP

Policy enforcement across web, email, personal cloud accounts, removable storage, printing, and AI tools, with three response types: block, educate the user in the moment, or allow an override with a written business justification.

Insider risk management

User risk scoring tied to behavior and data movement, with monitoring of workforce transitions, support for watchlists, and evidence packaged for Legal and HR.

Data security posture management

Discovery and classification of data at rest across SaaS, PaaS, IaaS, and AI systems, with remediation for over-shared and exposed data.

Endpoint agent, browser extension, and cloud connectors

Three modes working together: cloud API connectors into sanctioned applications, a browser extension for web activity, and an endpoint agent. Cyberhaven's own documentation describes that agent as designed to "securely perform processing in the cloud so it doesn't slow down devices."

Forensic capture

Optional screen capture of the 30 seconds before an incident, plus capture of the file itself for review.

Replacing or running alongside an existing deployment

Almost nobody evaluating endpoint DLP is starting from nothing. Most teams reading this already run Cyberhaven, a legacy DLP product such as Forcepoint or Symantec, Microsoft Purview endpoint DLP, or some combination. Running two endpoint agents sounds worse than it is, and a staged overlap is usually the fastest way to settle an architectural argument with evidence rather than slideware.

The approach we recommend:

1. Install Bold in monitoring mode alongside the incumbent. Both products see the same endpoints and the same user behavior for the same period. Nothing is blocked by Bold during this phase, so there is no risk to the business.
2. Compare on the same events, not on different reports. Pick the ten incidents the incumbent flagged last month and the ten files your security team would most hate to lose. Check which product classified each one correctly and how many of each product's alerts a human would call noise.
3. Test the edges on purpose. Take a laptop offline. Move a file with no origin story. Paste source code into a desktop AI application. These are the conditions where the two architectures diverge, and they rarely show up on their own during a quiet two-week pilot.
4. Port only the policies that actually fire. Most legacy DLP libraries contain hundreds of rules, of which a few dozen do all the work. Bold classifies without rules, so the migration is usually a shortlist of enforcement decisions rather than a rule-by-rule translation project.
5. Cut over by channel, not all at once. Web upload and email first, then removable media and printing, then desktop applications and developer workflows.
6. Keep what Bold does not replace. If the incumbent provides DSPM for cloud and SaaS data at rest, or cloud posture management, those stay. Bold replaces the endpoint layer.

Our honest take: lineage is real, the cloud round trip is the cost

Cyberhaven is a good product and the lineage idea is real. If your priority is a single vendor covering endpoint, SaaS, and cloud data at rest, with posture management and DLP under one contract, Cyberhaven covers more surface area than Bold does. We do not sell DSPM. If your risk centers on sanctioned cloud applications accessed from unmanaged devices and contractor laptops you will never install software on, their API connectors reach places an endpoint product cannot.

Cyberhaven also publishes outcome claims worth asking them to substantiate in your environment: roughly 90% fewer false positives, five times faster investigations, and 80% fewer incidents after user education.

The consolidation argument deserves a direct answer, because it is the one Cyberhaven makes best. Its former CEO said publicly that data security will consolidate rapidly, and the February 2026 platform release is that thesis in product form: one vendor covering DSPM, DLP, insider risk, and AI security. If your organization is carrying four tools, four contracts, and four consoles, that pitch is attractive and often correct. The counter-argument is not that consolidation is wrong, it is that consolidating on a weak endpoint layer is expensive to undo. The endpoint is where sensitive data is created and where most loss happens, and a suite is only as strong there as its endpoint architecture. Buy the suite if the breadth is what you need. Test the endpoint layer first, because that is the part you will live with every day.

Where we think the architecture favors Bold is narrower and sharper. If the data you are most afraid of losing is the data regex was never built to see, if your people work offline, on planes, or on untrusted networks, if the risk has moved into desktop applications and local AI tools rather than browser tabs, or if sending content to a vendor cloud for analysis is a problem for your legal team, those are the conditions Bold was built for.

The fastest way to settle it is a side-by-side test on the same laptop: classify a file that has no origin story, disconnect the network, and watch what each product does.

Cyberhaven alternatives

Bold: real-time data protection for the modern endpoint. Purpose-built AI models run locally to classify data by meaning, analyze intention, and prevent data loss at the moment of action, with full coverage offline and no content leaving the device to be understood. Best for teams whose risk sits in local files, desktop applications, and AI tools.

Microsoft Purview: the broadest labeling and compliance footprint in the enterprise, strongest if you are already deep in Microsoft 365 and need regulatory reporting. Most Purview customers we meet pair it with endpoint enforcement rather than replace it.

Netskope: strong cloud and web enforcement through a cloud service that inspects routed traffic. Covers the network layer well; data at rest on the device sits outside that inspection.

Forcepoint: mature legacy DLP with deep regulatory templates and channel coverage, usually chosen by organizations with established policy libraries and the staff to tune them.

Cyera: data security posture management for cloud and SaaS data stores, used to answer where sensitive data lives. It is a discovery and posture product, not endpoint enforcement, and is often deployed alongside one.

Code42 Incydr: file exfiltration monitoring with a long history in insider risk investigations, oriented to detection and response rather than preventive classification.

DTEX: workforce behavior analytics for insider risk, focused on user intent signals rather than the content of the data being moved.

The bottom line

One thing that does not show up in a feature comparison: Cyberhaven is no longer founder-led, and through 2026 it built out a full go-to-market leadership layer alongside its unified platform release. Expect a company pushing hard on consolidation rather than on any single capability.

Both products replace legacy, rule-based DLP. Cyberhaven reconstructs what data is by tracing where it came from, and analyzes that trace in its cloud across endpoint, browser, and SaaS connectors. Bold determines what data is by reading it on the device with local models, and enforces there in real time.

• For endpoint data loss prevention: Bold classifies proprietary data with no rules written for it, enforces offline and on untrusted networks, and covers local activity such as clipboard, USB, AirDrop, printing, and desktop applications that encrypt their own traffic. Cyberhaven enforces across the same channels with the decision made in its cloud, and adds coverage of sanctioned SaaS through API connectors.
• For insider risk management: Cyberhaven's lineage chain of custody is strong evidence when the data came from a connected system of record. Bold adds process-tree context on the device, so a file compressed and renamed at 2am by a departing engineer is tied to the human action that caused it.
• For AI usage control: Bold sees every AI tool running on the device, including desktop AI applications, AI browsers, and local coding assistants, and attributes each action to a person or to software acting on its own. Cyberhaven offers shadow AI discovery, prompt and upload controls, and coding agent governance delivered from its cloud.
• For cloud and SaaS data at rest: Cyberhaven sells DSPM and Bold does not. Companies that need both typically run an endpoint product and a DSPM product together.
• For regulated and privacy-sensitive environments: Bold keeps file content on the device, writes encrypted evidence to your own tenant under your key, and sends Bold maskable metadata. That is a different answer to a works council or a data residency review than cloud-side analysis with optional screen capture.

The decisive test takes two minutes on one laptop: classify a sensitive file that has no origin story, then pull the network and try to move it.

Sources: Cyberhaven homepage, How data lineage works, Cyberhaven Series D coverage

Bold vs. Cyberhaven FAQs

What is the main difference between Bold and Cyberhaven?

Where the AI runs and what it reads. Bold classifies and enforces on the device, reading the content itself. Cyberhaven collects on the endpoint and reasons in its cloud, classifying largely on the basis of where data originated.

Does Cyberhaven work offline?

Put the question to them directly. Cyberhaven's own documentation describes its endpoint agent as performing processing in the cloud, so offline behavior is worth testing rather than assuming. Bold classifies and enforces with no connection at all, because the models are on the device.

Can Bold classify data that has no lineage?

Yes. Bold reads the content itself, so a file drafted locally, pasted from a personal channel, or generated by a model is classified on what it contains. Lineage-first approaches are strongest when the origin is known and connected.

Is Cyberhaven a good DLP product?

Yes, and it is a serious one. It blocks, it coaches users in the moment, it supports justified overrides, and its investigation workflow is well regarded. The question is not whether it works, it is whether a cloud-decided architecture fits where your data actually moves.

Does Bold replace Cyberhaven?

For endpoint data loss prevention, insider risk, and AI usage control, yes, from a single deployment and a single shared context. For cloud and SaaS data at rest, no. Bold does not sell DSPM, and organizations that need posture management pair Bold with a DSPM product.

Which is better for AI usage control?

Both cover it and both should be tested. Bold sees every AI interaction on the device, including desktop AI applications and local coding assistants, and uses process-tree analysis to attribute an action to a person or to software acting on its own. Cyberhaven offers shadow AI discovery, prompt and upload controls, and coding agent governance delivered through its cloud.

How long does each take to deploy?

Bold installs in seconds and classifies on day one with no rule library or baseline period. Cyberhaven's deployment spans an endpoint agent, a browser extension, and cloud API connectors, and the company now offers configuration agents to reduce that setup work.

Does our data leave the company?

With Bold, no content does. Classification happens on the device, encrypted evidence goes to your own cloud tenant under your own key, and Bold receives metadata that can be masked. With Cyberhaven, analysis happens in its cloud, with optional capture of screenshots and files for incident review.