Cyberhaven Reviews 2026

Key Takeaways

• Data lineage is Cyberhaven's most prominent documented differentiator - the platform tracks data origin, transformations, and destinations across users and applications, anchoring its investigation workflows. These are vendor-documented capabilities.
• Cyberhaven expanded its portfolio during 2026 - a February unified platform combining DSPM, DLP, insider risk, and AI security, followed by AI agent and MCP discovery, local IDE and desktop agent coverage, and Flow, announced in July for availability in a following quarter.
• A December 2024 Chrome extension compromise was narrower than commonly reported - roughly 400,000 users or installations were potentially exposed to a malicious extension version that was available for approximately one day before removal. Actual credential theft was never publicly quantified.
• Deployment is a phased program measured in weeks to months - Cyberhaven describes meaningful coverage in weeks and frames implementation guidance as a phased program, with third-party review data describing implementation measured in months.
• The public evidence base is limited in volume - Gartner Peer Insights lists 4.6/5 from 43 reviews and G2 lists 4.5/5 from 18 reviews as of July 30, 2026, while widely repeated reviewer counts trace to an aggregation page that does not link claims to identifiable reviews.
• Nightfall runs one detection brain across every surface - data detection and response through native SaaS APIs deploys in minutes without endpoint agents, proxies, or network changes, and AI agent and MCP security covers local stdio and remote HTTP and SSE workflows plus IDE hooks with full inline blocking.
• AI moves your data. Nightfall controls it. Nightfall delivers 95% detection precision out of the box, cuts false positives by 95%, and governs both actors that now move sensitive data: humans and AI agents.

The data loss prevention market has shifted since 2024. AI has not only changed how data moves, it has changed who moves it. Sensitive data now flows through copilots, coding assistants, agents, and MCP servers at machine speed, with no human in the loop. Legacy DLP was built for one actor. The reality of 2026 has two.

Cyberhaven built its reputation on data lineage tracking, and during 2026 it moved into agentic AI security. The question for buyers is no longer whether Cyberhaven addresses AI workflows at all. It is how much of that capability is generally available today, how deep the enforcement goes, and how it compares to a platform architected for cloud-native and agentic data exfiltration prevention from the outset.

This review examines Cyberhaven's current capabilities, documented user feedback, its December 2024 security incident, and how the platform compares to an AI data security platform built for the way data moves now. The goal is straightforward: help security teams determine which architecture matches the surfaces their data actually crosses.

Cyberhaven: A Look at DLP 2.0 in 2026

Cyberhaven has long described itself as the inventor of the industry's first Data Detection and Response platform and helped popularize DDR as an alternative to conventional DLP. The term is now used more broadly across the industry, including by IBM and Palo Alto Networks, so Cyberhaven's use and promotion of the category is well documented even though sole authorship of the term is not independently established. For background on the category itself, see the DDR essential guide.

The core value proposition centers on data lineage tracking that follows information from its origin through transformations, copies, and transfers across endpoints, SaaS applications, and cloud infrastructure.

What Cyberhaven documents well:

• Origin-to-destination data lineage - tracks how data moves and transforms across the environment, providing context that content-only scanning misses
• Cross-platform endpoint support - Windows, macOS, and Linux endpoints are publicly documented, and Cyberhaven states in its own competitive materials that it delivers cross-platform feature parity. A comprehensive public OS-by-feature matrix is not available.
• Unified platform architecture - the February 2026 launch combined DSPM, DLP, insider risk management, and AI security
• Behavioral context - uses data flow patterns and behavioral signals, not only content matching

As of July 30, 2026, Gartner Peer Insights lists Cyberhaven at 4.6/5 from 43 reviews in its Data Loss Prevention comparison view, while G2 lists 4.5/5 from 18 reviews. Review counts on both platforms are dynamic, so these figures read as a dated snapshot.

Cyberhaven has a relatively small number of public reviews compared with some software vendors. Review volume alone, however, is not a reliable proxy for installed endpoints, customer count, revenue, enterprise penetration, or market share. Review totals are shaped by vendor solicitation practices, platform incentive programs, listing history, and average contract size, so no adoption conclusion follows from the count itself.

Beyond Legacy: How Cyberhaven Addresses Evolving Data Loss Prevention Needs

Traditional DLP relied on content inspection and static rules. Cyberhaven's contribution was recognizing that where data came from provides context that content inspection alone cannot supply. A document copied from a classified share and pasted into ChatGPT carries a different risk profile than the same text typed from memory. That framing is analysis rather than a measured product fact, but it accurately describes the design intent Cyberhaven documents. Nightfall approaches the same insight from the detection side with context-aware detection that replaces regex with models.

The DLP 2.0 approach introduces several improvements over legacy systems:

• Context-aware detection - understands data origin and transformation history, not only current content
• Reduced false positives - Cyberhaven reported a 90% reduction in false positives in a July 2025 announcement and currently advertises up to 95% fewer false positive alerts. These are vendor-reported results.
• Insider threat visibility - forensic reconstruction of data movement supports investigation workflows
• Cloud and SaaS coverage - API connectors extend beyond on-premises networks

Cyberhaven originated with an endpoint-centered data lineage architecture, describing its own advantage as starting from the endpoint out, and now combines endpoint agents, browser extensions, and cloud API connectors as distinct telemetry and deployment modes.

AI agents, copilots, and MCP servers move data through pathways that traditional endpoint DLP was never designed to observe, and Cyberhaven has responded to that shift. Its March 2026 agentic AI security announcement describes discovery and inventory of AI agents, MCP servers, and their connections on endpoints, along with monitoring of data access, tool use, and execution paths plus runtime controls. Its May 2026 announcement extends to agents running locally in developer tools, IDEs, and desktop applications, and describes MCP server inventory, agent risk scoring, reconstruction of agent execution lifecycles, and prompt and response controls.

What remains open is availability and enforcement depth. Publicly accessible Cyberhaven materials do not provide a transport-by-transport matrix distinguishing local stdio MCP from remote HTTP and SSE MCP, and the newest Cyberhaven Flow capabilities were announced for availability in a coming quarter rather than as generally available. The reason that distinction matters is architectural, as explained in how MCP bypasses traditional security.

Cyberhaven vs. Nightfall AI: Governing Data Movement in the Age of AI

The comparison reveals an architectural difference rather than a simple capability gap. Cyberhaven's lineage depth comes from deep endpoint instrumentation. Nightfall leads with one detection brain that runs everywhere data moves, across SaaS, endpoint, browser, email, AI applications, and every MCP and agent workflow, with enforcement applied in real time.

Lineage depth is real, and so is the cost of getting it. Exhaustive lineage produces a large volume of events, and lineage alone does not stop a file from leaving. Nightfall inverts the design: AI-native detection decides what is risky first, so the lineage teams act on is the lineage that matters. Visibility without control is just a dashboard.

Cyberhaven's Approach to AI Agent Workflows

Cyberhaven provides visibility into AI tools through its endpoint agents, browser coverage, and ChatGPT integration, and tracks these interactions as part of its overall data lineage model. During 2026 it extended this to agentic workflows, including local agents inside developer tools and IDEs.

The open questions are availability and depth, not absence. Cyberhaven's public materials do not document MCP transport coverage connector by connector, and Flow was announced for a later availability window. For organizations running Claude Code, Cursor, GitHub Copilot, Claude Cowork, or custom MCP servers, the practical issue is that the moment data moves through an AI agent, a lineage-first architecture has limited ability to monitor, block, or trace it. That surface is the fastest-growing exfiltration vector in the enterprise, as described in MCP security in 2026.

Nightfall AI's Unified Control Platform

Effective AI agent and MCP security requires visibility into both local stdio and remote HTTP or SSE MCP workflows, IDE hooks, per-server risk scoring, tool classification, and prompt injection detection. Nightfall covers local stdio and remote HTTP and SSE MCP discovery, hooks for Cursor, Claude Code, and VS Code on macOS and Windows, per-server risk scoring, and a shared policy framework spanning endpoint, SaaS, and AI agent surfaces.

Nightfall risk scores each MCP server by what its tools can actually do, distinguishing read, read and write, and destructive operations, and hooks scan or block prompts, MCP tool calls, tool responses, and shell commands. This is full inline blocking, not just visibility and not just alerts, which gives the CISO a defensible answer to the question of whether AI agent risk is governed. The fundamentals are laid out in AI agent security explained.

The distinction that matters is architectural. Nightfall applies a unified detection and policy layer across SaaS, endpoint, browser, email, AI application, and AI agent or MCP workflows, rather than extending a single deployment mode to new channels. Lineage is not exclusive to endpoint-instrumented platforms either: Nightfall delivers AI-based data lineage that traces information from source to destination and across transformations. The full breakdown sits in the Nightfall vs Cyberhaven comparison.

Endpoint Security in 2026: Cyberhaven's Footprint and Features

Cyberhaven's endpoint agent represents the platform's most mature capability. Windows, macOS, and Linux support is publicly documented, and Cyberhaven states that it provides cross-platform feature parity. A public OS-by-feature matrix covering every enforcement channel, removable media control, print control, screen evidence, browser coverage, application control, and AI agent observability is not available.

Documented endpoint agent capabilities include:

• Clipboard monitoring - tracks copy and paste operations involving sensitive data
• File activity tracking - monitors creation, modification, and transfer of files
• Application visibility - identifies which applications access sensitive content
• Screen and screenshot evidence capture - records contextual visual evidence around incidents to support investigation. This is evidence recording rather than inspection of screenshot image contents.
• USB and removable media control - governs data transfer to external devices

Documented endpoint themes from user feedback:

An aggregated review analysis page references endpoint resource utilization as a discussion theme and attributes a count of reviewers to it. Because the page does not transparently link each count and quotation to an identifiable review, these findings read as directional rather than as verified reviewer statistics. Cyberhaven describes its endpoint agent as lightweight.

Public sentiment on the interface is mixed rather than uniformly negative. G2 exposes tags covering setup and configuration experience alongside compatibility topics, while other G2 material describes the interface positively and highlights forensic visibility.

For endpoint-focused use cases, Cyberhaven delivers documented coverage across the traditional exfiltration channels. Nightfall's endpoint and browser DLP approaches the same surface with a single agent that covers both human and AI or MCP traffic across more than 10 vectors, running at roughly 1% CPU and 50MB RAM with macOS and Windows parity, and deploying in about 30 minutes through MDM tooling such as Jamf and Intune. Detection is ML and LLM based rather than behavior or lineage only, which is why coverage of modern channels does not depend on rebuilding rules per surface. Broader context is in the endpoint DLP essential guide.

Data Protection Platforms: Evaluating Cyberhaven's Scope and Efficacy

Cyberhaven's February 2026 unified platform launch brought DSPM, DLP, insider risk management, and AI security into a single architecture, with stated coverage across endpoints, SaaS, cloud, on-premises environments, and AI workflows. Consolidation addresses a real market need: security teams managing multiple point solutions face tool sprawl, fragmented visibility, and operational complexity.

The unified platform includes:

• Data Security Posture Management (DSPM) - discovers and classifies sensitive data across cloud, SaaS, and on-premises environments
• Data Loss Prevention (DLP) - enforces policies to prevent unauthorized data movement
• Insider Risk Management (IRM) - identifies risky user behavior patterns and potential insider threats
• AI Security - governs data movement to and from AI applications and agents

The vision is compelling, and the consolidation logic is sound. The economics are worth noting: Cyberhaven's AI capability is packaged separately from the endpoint license, so the buyer ends up with two cost lines. Nightfall is AI-native by design, and the AI is included in every tier.

Sequencing matters as much as packaging. Prevention does not require posture as a prerequisite. Cataloging data at rest before enforcement begins is the wrong order of operations when agents are already moving data. Nightfall starts preventing on day one and delivers data discovery and classification as a byproduct of prevention, which consolidates DLP, insider risk, and AI governance into one stack rather than three contracts.

Reported execution is mixed, and this is analysis rather than measured fact. Some reviewers and third-party summaries reference setup or interface complexity, while other reviewers highlight Cyberhaven's visibility and investigation context. Cyberhaven's own February 2026 announcement includes a named CISO testimonial praising the familiarity of its visual data representation. The available review population is too small and inconsistently sourced to characterize sentiment as uniform in either direction.

The question security teams face is whether platform consolidation delivers value if operational complexity offsets efficiency gains. Nightfall answers it with AI-native triage: policies are built and customized to the environment automatically, incidents arrive with a complete forensic story covering user, role, data lineage, and prior behavior, and Nyx surfaces high-risk users and recommends policy so SecOps moves from triage to oversight.

SaaS Data Security in 2026: Cyberhaven's Role in Cloud Environments

SaaS data security requires different approaches than endpoint protection. Data at rest in Google Drive, Slack, or Microsoft 365 needs scanning, classification, and remediation capabilities that endpoint agents cannot directly provide.

Cyberhaven extends SaaS coverage through API-based integrations that connect its data lineage model to cloud applications.

SaaS coverage considerations:

• Connector availability - coverage depends on the published integration catalog for each SaaS platform
• Scan behavior - Cyberhaven's publicly accessible materials do not document polling or synchronization intervals, event-driven versus periodic scanning, or historical scan behavior connector by connector
• Remediation options - the public catalog does not provide a connector-by-connector matrix of supported actions such as quarantine, deletion, revocation, label changes, permission changes, redaction, or encryption

Nightfall takes a different path with native APIs that avoid endpoint agents, proxies, and network changes. SaaS integrations deploy within minutes, with real-time and historical scanning across 13 applications including Slack, Google Drive, and Microsoft Teams. Granular remediation covers redaction, deletion, access revocation, quarantine, and encryption, delivered through admin, automated, or end-user driven workflows. Microsoft-heavy estates get context on the native option in more than Purview.

For organizations with substantial SaaS footprints, the architectural starting point matters. An endpoint-first platform extending to SaaS through connectors operates differently from a platform built API-native for cloud environments and then extended, with the same detection brain, to endpoint and agentic surfaces.

Insider Threat Prevention: Can Cyberhaven Mitigate New Risks?

Insider threat detection represents Cyberhaven's core use case, and the capability set here is well documented. Cyberhaven publishes behavioral context and risky behavior detection, insider risk management, and forensic file capture, screen recordings, evidence capture, and incident reconstruction.

Insider threat capabilities include:

• Data flow forensics - reconstructs data movement history for investigation
• Behavioral analysis - identifies patterns that may indicate data theft or unauthorized access
• User risk scoring - aggregates activity signals to surface high-risk users
• Incident investigation - provides evidence trails for security and legal teams

Human risk and AI risk are not two problems, they are one. Solve human risk alone, or AI risk alone, and exposure remains on the side that was ignored. Nightfall addresses both together by design, pairing continuous telemetry that captures all data movement rather than only policy violations with HRIS and IdP metadata, session replay, and endpoint lineage. The combination is described in forensic search and app intelligence, alongside Nightfall's approach to insider risk.

The December 2024 Chrome extension incident is worth examining accurately. A Cyberhaven employee was targeted through phishing or malicious OAuth consent, the attacker obtained Chrome Web Store publishing access, and a malicious extension version was published. The extension was designed to exfiltrate cookies, session tokens, and related authentication information, particularly in connection with targeted social media advertising and AI services. The incident was part of a broader campaign against Chrome extension developers.

Key incident details:

• Attack vector - phishing or malicious OAuth consent targeting an employee with Chrome Web Store access
• Store availability window - published December 24, 2024 and removed the following day after internal detection
• Activity on affected installations - malicious code reportedly remained active on some installations for approximately one day after publication
• Exfiltration domain - active for under a day
• Exposure population - the extension had roughly 400,000 users or installations that were potentially exposed. The number that actually received the malicious update, visited a targeted site during the window, or had credentials or session data actually exfiltrated was not publicly established.

This incident does not invalidate Cyberhaven's insider threat capabilities. It does highlight that security vendors themselves face supply chain risk in privileged components, a point developed in lessons from the incident. The broader takeaway for security teams is that every privileged component in the data path, including browser extensions and agent runtimes, belongs inside the same governance and risk program that covers the rest of the estate.

Cyber Security Solutions for Businesses: Pairing Cyberhaven with Your Ecosystem

Integration capabilities determine how well any DLP solution fits into existing security operations.

Cyberhaven's publicly listed integrations include:

• Microsoft - Microsoft 365, OneDrive, and Outlook, plus documented Microsoft Purview integration and Purview label support
• Google - Google Workspace, Gmail, and Google Drive
• Collaboration and developer tools - Box, GitHub, GitLab, Slack, and ChatGPT
• SIEM and SOAR - Splunk, Elastic, and LogRhythm
• Identity and HR systems - numerous directory and HR integrations

Three commonly repeated integration claims need qualification. Salesforce appears in a Cyberhaven data lineage example but not in the current public application integration catalog. Cyberhaven is purchasable through the AWS, Azure, and Google Cloud marketplaces and publishes multi-cloud DSPM material, though marketplace availability is a procurement mechanism rather than an indication of specific datastore connectors such as S3, Azure Blob Storage, BigQuery, or Cloud Storage. And while the catalog lists SIEM and SOAR products, it does not visibly list an ITSM or ticketing integration.

Policy configuration is another area where the common narrative outruns the evidence. Competitor comparison pages assert that Cyberhaven requires SQL-like query knowledge, but that claim comes from a competing vendor without identifiable underlying reviews. Cyberhaven's current DLP product page instead describes a visual policy editor, Boolean logic, autocomplete, and conversion of graph queries into policies. Cyberhaven supports advanced lineage and graph query workflows, and publicly accessible evidence does not establish that routine policy configuration requires SQL expertise.

Deployment expectations follow from Cyberhaven's own materials, which describe meaningful coverage in weeks and frame implementation guidance as a phased program, aligning with third-party review data describing implementation measured in months. Nightfall's model is different in kind rather than degree: SaaS integrations authorize and begin scanning within minutes, the endpoint agent deploys in about 30 minutes through MDM, and policies are generated and tuned by AI rather than hand-built per surface. Response reaches analysts and end users where they already work through Slack, Teams, email, Jira, and on-device notifications, with an API and MCP server available for SOAR and ITSM workflows.

Why Security Teams Are Exploring Cyberhaven Alternatives in 2026

The DLP market has evolved significantly since Cyberhaven established its position. Nightfall's guide to Cyberhaven alternatives captures much of the historical evaluation context, and the current Nightfall vs Cyberhaven comparison reflects the 2026 picture. Several factors continue to drive evaluations:

The agentic surface - Cyberhaven has announced agentic AI, local agent and IDE, and MCP visibility capabilities during 2026, with Flow announced for availability in a following quarter. Nightfall already covers local stdio MCP, remote HTTP and SSE MCP, IDE-embedded agents, and desktop AI applications with the same detection brain and full inline blocking. Background on that risk model sits in AI agent exfiltration risk.

Time to value - a phased rollout measured in weeks to months compares differently against SaaS coverage that begins within minutes and endpoint coverage that deploys in about 30 minutes.

Detection quality - 95% precision out of the box, ML detectors for PII, PHI, secrets, credentials, and financial data, plus LLM classifiers across more than 20 categories, means the queue contains signal rather than noise. Detectors are customer-trainable and auto-retraining.

Commercial simplicity - AI-native detection is included in every Nightfall tier rather than licensed as a separate line, which consolidates legacy DLP, insider risk, and AI governance into a single contract.

GenAI adoption acceleration - ChatGPT, Claude, Copilot, and other AI applications create data movement channels that require purpose-built governance, alongside the shadow AI that appears without a procurement trail.

Nightfall reports that organizations evaluating or adopting its platform have cited deployment effort, documentation, telemetry, and coverage among their reasons for looking at alternatives. These accounts, summarized on Nightfall's comparison hub, are anonymous and may predate portions of Cyberhaven's 2026 expansion.

Nightfall AI: AI-Native Data Security for Modern Data Movement

Nightfall is the AI data security platform that gives enterprises real-time visibility and control over data movement by humans and AI agents, MCP servers, SaaS, email, and endpoints. Built API-first for cloud-native environments, it prevents sensitive data exposure and data exfiltration across every surface through one detection brain and one policy layer.

Key differentiators that distinguish Nightfall:

• 95% detection precision out of the box, using more than 100 AI-based models, LLM-based file classifiers, and computer vision models, with false positives cut by 95%
• SaaS deployment in minutes, with a lightweight endpoint agent, browser plugins, and MDM-supported endpoint and browser coverage through tools such as Jamf and Intune
• Four in five incidents resolved through automation or employee self-remediation, so SecOps evolves from triage to oversight and governance
• Full control plane for agents, covering local stdio and remote HTTP and SSE MCP security, IDE hooks for Cursor, Claude Code, and VS Code, per-server risk scoring, tool classification by read, read and write, and destructive capability, prompt injection detection, and inline blocking across prompts, tool calls, tool responses, and shell commands
• Unified coverage across every surface, from SaaS and endpoint to browser, email, AI applications, and MCP workflows, driven by one detection engine
• Lower total cost of ownership, with AI included in every tier and a configurable ROI calculator on the pricing page based on customer benchmarks

Nightfall uses AI-native detection powered by supervised fine-tuned models, which lets teams secure data flows in minutes, uncover shadow AI and agent chains, and distinguish legitimate business activity from dangerous exfiltration without slowing innovation. More than 100 organizations run on Nightfall, including Gusto, DraftKings, Grafana Labs, Grab, Nubank, and Decagon.

Nightfall's architecture is designed to reduce deployment and operational overhead: native SaaS APIs remove the need for proxies and network changes, AI-based detection removes false positive investigation time, and automated or employee-driven remediation handles routine incidents without analyst intervention. The endpoint, browser, and AI agent surfaces are covered by a single lightweight agent running at roughly 1% CPU and 50MB RAM, so coverage expands without expanding operational cost. That design is described further in comprehensive exfiltration prevention.

For organizations evaluating a move, Nightfall publishes a practical migration blueprint covering migration consultation, policy translation assistance, custom control mapping, transition support, and a recommended parallel-running period.

The core question organizations face: was your current DLP solution built for how data moves today? Legacy DLP was architected when humans were the primary actors moving data through known channels. AI agents, copilots, and autonomous workflows now move sensitive data at machine speed, and a compromised workflow can exfiltrate in seconds what would take an employee years. Speed is the threat, and speed is the only defense that works. Seeing the leak is not the win. Stopping it is. Teams ready to see that in their own environment can request a demo.

Frequently Asked Questions

How does Cyberhaven's pricing compare to alternatives, and what should buyers expect for total cost of ownership?

Cyberhaven does not publish standard list prices. Its enterprise terms state that direct purchase fees are based on endpoint users and endpoint usage, with overage provisions defined in the applicable order form, so endpoint-based commercial terms are supported, and final rates, discounts, minimums, and bundles vary by order form. Its AI capability is packaged separately from the endpoint license, which produces a second cost line. The commonly repeated claim that Cyberhaven pricing becomes opaque specifically above 10,000 endpoints traces to an aggregation page without an identifiable underlying review. Nightfall's pricing page describes package tiers, configurable coverage, and an ROI calculator, and AI-native detection is included in every tier rather than sold as an add-on. Total cost of ownership should factor implementation effort, training investment, and ongoing operational overhead alongside licensing, which is where consolidating DLP, insider risk, and AI governance into one platform changes the arithmetic.

What compliance frameworks does Cyberhaven support, and how does coverage compare to other DLP solutions?

Cyberhaven documents capabilities intended to support GDPR, HIPAA, PCI DSS, SOX, and other requirements through classification, controls, lineage, and audit evidence. Its data lineage capabilities suit requirements that demand audit trails of data movement, and coverage depends on connector availability for the applications and data stores in scope. Nightfall supports the same regulatory landscape with real-time enforcement across SaaS, endpoint, email, and agentic surfaces, so the evidence trail reflects data movement as it happens rather than a periodic snapshot. Relevant references include HIPAA, SOC 2, a PCI compliance checklist, and a broader guide to DLP for compliance. No DLP solution automatically makes an organization compliant; each provides tools that support compliance-related workflows when properly configured and maintained.

Is Cyberhaven suitable for small and medium businesses, or is it primarily an enterprise solution?

Cyberhaven appears primarily oriented toward mid-market and enterprise organizations based on its public customer examples, sales process, endpoint-based commercial terms, professional services offerings, and G2 reviewer demographics, though it is not formally restricted to that segment and G2 does include some small business reviewers. Smaller and fast-growing organizations often weigh time-to-value and operational overhead against internal expertise, which is where a platform that begins protecting SaaS data within minutes and generates its own policies fits a lean security team. Nightfall's prevent data exfiltration anywhere approach is designed for exactly that profile: security-conscious, innovation-forward organizations where sensitive data moves fast and AI adoption is outpacing governance.

How do AI governance requirements affect DLP solution selection in 2026?

AI governance has become a primary driver for DLP evaluation as organizations adopt ChatGPT, Claude, Copilot, coding assistants, and internal AI applications. The critical question is whether a platform can see and control data movement through the specific AI workflows an organization actually runs. Cyberhaven describes AI agent and MCP server discovery on endpoints plus local agent and IDE coverage. Nightfall governs local stdio and remote HTTP and SSE MCP with IDE hooks, per-server risk scoring, tool classification, prompt injection detection, and inline blocking, which turns "are we governing AI agent risk?" into a question answered with control rather than discovery. Practical guidance for building that program is in MCP security for CISOs, securing AI agents, and the 2026 AI agent report.

What happens to existing Cyberhaven policies and data if an organization decides to migrate to a different platform?

Publicly accessible Cyberhaven materials, including its support and documentation portals, do not establish whether policies, incidents, lineage records, or historical telemetry can be exported in a reusable format, and its enterprise terms confirm customer ownership of customer data without specifying an export mechanism. On the destination side, Nightfall's migration blueprint sets out a structured path: migration consultation, policy translation assistance, custom control mapping, transition support, and a recommended parallel-running overlap period so coverage is validated before the previous platform is decommissioned. Because Nightfall builds and customizes policies with AI rather than requiring rule-by-rule reconstruction, translating existing detection logic and remediation workflows is part of the onboarding path rather than a separate project.

Does data lineage replace the need for AI-native detection?

No. Lineage shows where data has been; detection decides whether that movement matters. Lineage alone does not stop a file from leaving, and exhaustive lineage generates a large event volume that still needs prioritization. Nightfall orders the two deliberately: AI-native detection determines what is risky first, lineage shows the trail on what matters, and enforcement stops the movement in real time. The same detection brain runs on every surface, including the agentic ones, which is why coverage of secure AI usage does not require a second product or a second contract.