Securiti AI Alternatives

Securiti AI, acquired by Veeam in a $1.725 billion transaction completed on December 11, 2025 and now marketed as Veeam's Securiti AI, has built a strong reputation in data privacy, governance, and DSPM. Its standing was reinforced when, according to a Securiti press release, Veeam's Securiti AI was named a Leader in GigaOm's 2026 DSPM Radar. The platform spans privacy automation alongside data security, AI security and governance, Agent Commander, and LLM firewalls.

Even so, buyers evaluating this category are asking a sharper question in 2026: how natively and how directly does a platform enforce policy on sensitive data as it actually moves through SaaS applications, email, endpoints, browsers, AI applications, and agentic or MCP workflows? AI has not just changed how data moves. It has changed who moves it. Data now flows through copilots, agents, and MCP servers at machine speed, with no human in the loop. Legacy DLP was built for one actor. The new reality has two.

That shift makes enforcement depth, channel coverage, deployment model, and detection quality the separating factors, far more than a vendor's original product category. For security teams facing Shadow AI adoption, AI agent proliferation, and aging DLP architectures, choosing the right AI data security platform has become a control architecture decision rather than a governance one.

This guide examines seven alternatives that address different data security requirements in 2026, starting with Nightfall AI, the control platform for sensitive data that governs how data is accessed, moved, and exposed across human activity and AI agent workflows.

Key Takeaways

• Enforcement depth matters more than category labels: Visibility without control is just a dashboard. Governance, DSPM, and SSE platforms each cover part of the movement problem, and Nightfall is built to control all of it across both humans and AI agents, in real time, on every surface.
• AI agents create new data movement risks: Sensitive data is now moved autonomously through prompt injections, chained agent flows, and MCP tool calls. Nightfall covers local stdio MCP, IDE embedded agents, and remote HTTP workflows with the same detection and response engine that runs everywhere else.
• Detection quality determines operational burden: Nightfall delivers 95% precision out of the box against a 5% to 25% legacy DLP baseline, and cuts false positives by 95%. Higher precision reduces alert volume, alert fatigue, and manual investigation work.
• Deployment speed impacts time to value: An API first architecture lets Nightfall customers connect a SaaS application in minutes, with an initial scan surfacing exposures within 24 hours and full endpoint fleet coverage within a week.
• Unified platforms reduce vendor sprawl: Nightfall consolidates DLP, insider risk, and AI governance into one stack, replacing three contracts, three vendor relationships, and three budget lines with a single control plane.
• Privacy operations and data movement control are distinct buying centers: Securiti's roots are in privacy automation, DSR fulfillment, consent management, and DPIA workflows. Organizations may run Securiti and Nightfall as complementary platforms when they want separate privacy operations and data movement control layers.

1. Nightfall AI

Nightfall AI is the AI data security platform that provides enterprises real time visibility and control over data movement by humans and AI agents across SaaS, email, endpoints, browsers, MCP servers, and AI applications. AI moves your data. Nightfall controls it.

How Does Nightfall AI Work?

One detection brain runs across every surface: supported SaaS integrations, email environments, macOS and Windows endpoints, browsers, AI applications, and agent and MCP workflows. The platform combines ML detectors for PII, PHI, secrets, credentials, and financial data with LLM file classifiers spanning more than 20 categories, plus computer vision for images and screenshots. Key capabilities include:

• SaaS Data Security: Real time and historical scanning across 13 supported applications including Slack, Google Workspace, Microsoft 365, Salesforce, Jira, Confluence, and Zendesk. Granular remediation spans blocking, redaction, deletion, access revocation, quarantine, encryption, and user coaching, through admin, automated, or end user driven workflows. See the full set of Nightfall integrations for current coverage.
• Endpoint Data Security: A single lightweight agent covers human and AI or MCP traffic across 10 or more vectors including clipboard activity, browser uploads and downloads, cloud sync services, USB transfers, printing, and screen captures. Nightfall runs at roughly 1% CPU and 50 MB of RAM with macOS and Windows parity, and deploys through MDM tools such as Jamf and Intune in about 30 minutes. Details are on the endpoint and browser DLP page.
• AI Agent and MCP Security: Discovery and enforcement for local stdio MCP servers and remote HTTP/SSE or Streamable HTTP workflows, IDE hooks for Cursor, Claude Code, and VS Code, MCP server risk scoring, and classification of tool actions as read, read/write, or destructive. Prompt injection detection runs on agent traffic, with full inline blocking rather than alerts alone. See the MCP security product page for current scope.
• GenAI Application Protection: Browser plugins and endpoint agents monitor AI interactions in real time across ChatGPT, Microsoft Copilot, Claude, Gemini, DeepSeek, Perplexity, and Grok, inspecting prompts, file uploads, and clipboard content and supporting prompt sanitization or redaction before submission.

Detection and Remediation Capabilities

Nightfall delivers 95% precision out of the box, compared with a 5% to 25% baseline for legacy DLP approaches built on regex and static rules. Detectors are customer trainable and auto retraining, so accuracy compounds with use rather than degrading as environments change. Real time controls include:

• Block sensitive data before it leaves
• Coach users with contextual guidance
• Override workflows with manual or automated approval
• Redact, encrypt, quarantine, or delete sensitive content

Across agentic workflows, Nightfall scans and enforces policy on prompts, MCP tool calls, MCP tool responses, and shell commands, giving the CISO a defensible answer to "are we governing AI agent risk?" backed by control, not discovery.

Reported Results

Organizations using Nightfall report the following outcomes:

• Victor Sogaolu, Staff Security Engineer at Snyk: "Nightfall is reliable. When it says there's a detection, we trust that detection." Read the Snyk case study for the full deployment story.
• Jay Crumb, Head of Security at Unit21: "Nightfall gently redirects our people to the safe gen AI sites and helps us by blocking attempts from folks putting PII or customer data into ChatGPT." Unit21 reports 67% of incidents automatically remediated.
• Nightfall reports 20x average ROI, with organizations generally seeing 6x ROI within the first 90 days, modeled on an 85% reduction in manual investigation time. See the ROI calculator for the underlying assumptions.
• 80% of incidents are resolved through a combination of automated remediation and employee self remediation.

Privacy and Compliance

Nightfall provides PHI detection and policy controls that support HIPAA compliance programs for healthcare organizations, and payment card data controls that support PCI DSS reporting for financial services. Detection categories span PHI, PII, PCI and financial data, credentials, secrets, and proprietary information across supported channels, with SOC 2 and ISO 27001 program support alongside them.

Best For: Organizations seeking real time control over data movement across SaaS, endpoints, browsers, and AI workflows. Ideal for security teams facing Shadow AI adoption, AI agent proliferation, or aging DLP architectures. Particularly suited for digital health, fintech, and software and developer platforms where sensitive data moves fast and AI adoption is outpacing governance.

2. Strac

Strac positions itself as a unified DLP and DSPM platform covering SaaS, cloud infrastructure, GenAI applications, and MCP workflows, with remediation capabilities across managed surfaces.

Key Features

• SaaS Coverage: A broad integration catalog including Slack, Google Workspace, Microsoft 365, Salesforce, GitHub, and Zendesk
• Endpoint Protection: Documented support for Windows, macOS, and Linux
• GenAI and MCP Security: Browser based and endpoint based protection, plus inspection and redaction at MCP tool call boundaries, including coverage for sensitive data retrieved from connected applications
• Remediation Options: Inline redaction, masking, warning, blocking, and vault or tokenization workflows
• Data Discovery: OCR and ML based classification across cloud environments, including protections aimed at AI agents

Deployment and Pricing

Strac uses a sales led pricing process for its DLP platform in the public materials reviewed. Strac Comply has separately marketed compliance package pricing, which is packaged apart from the full DLP and DSPM platform. Strac supports SaaS integration deployment alongside its endpoint coverage.

Considerations

Strac is a more recent entrant in the enterprise DLP market. The distinction that matters most in 2026 is architectural: point coverage of individual surfaces leaves the crossover invisible, because the same employee runs a local MCP server in Cursor, fires prompts at a remote LLM, and pulls a file off the endpoint in a single afternoon. Nightfall runs one detection brain across all of it, with full inline blocking on the agentic surfaces and AI capability included in every tier rather than priced as a separate line.

Best For: Mid market to enterprise organizations seeking unified DLP and DSPM functionality and broad SaaS integration coverage.

3. BigID

BigID delivers enterprise data intelligence and DSPM capabilities at petabyte scale, and also markets active data security controls alongside discovery and classification.

Core Capabilities

• Data Discovery: Coverage across a large catalog of native data sources spanning cloud, SaaS, databases, data lakes, and other enterprise repositories
• Classification Engine: Thousands of classifiers across more than 100 languages with ML and NLP based detection
• Identity Aware Classification: Links PII fragments to real individuals for DSAR fulfillment
• Prompt Based Classification: Define sensitive data categories in natural language without regex
• Data Cataloging: Lineage tracking and business glossary integration
• Active Controls: Cloud DLP, AI prompt security, policy enforcement, remediation, and data access governance within its broader data security platform

Enterprise Focus

BigID targets large enterprises requiring comprehensive data estate mapping. The platform is oriented toward understanding where sensitive data lives across complex hybrid multi cloud environments, providing an intelligence layer that feeds governance and compliance programs, with enforcement and remediation layered on that foundation.

Considerations

Prevention does not require posture as a prerequisite. Spending six to twelve months cataloging data at rest while exfiltration goes unprevented is the wrong order of operations, and static labeling becomes out of date the moment an agent moves the file. Nightfall starts preventing on day one, and data discovery and classification arrives as a byproduct of prevention rather than a prerequisite for it. Keep a DSPM program if it serves the business. Just do not delay data exfiltration prevention waiting on it.

Best For: Large enterprises requiring petabyte scale data discovery, comprehensive DSPM capabilities, and identity aware classification for privacy program support.

4. Varonis

Varonis provides data security with deep access governance capabilities, and by 2026 also markets a full stack AI security platform.

Platform Capabilities

• Access Governance: Permissions mapping across file shares, collaboration platforms, and cloud storage
• Exposure Reduction: Automated least privilege remediation for stale data and excessive permissions
• Classification: Data classification enriched with identity, permissions, and usage context
• UEBA: User and entity behavior analytics for insider threat detection
• DLP and DSPM: DLP and data security posture management alongside SaaS and cloud data security
• AI Security: Atlas, made generally available in May 2026 as an end to end AI security platform following the AllTrue acquisition

Strengths

Varonis is oriented toward understanding who can access what data and whether those permissions are appropriate. Its strength lies in reducing exposure through automated least privilege enforcement and stale data cleanup, particularly for Microsoft 365 and on premises file share environments, with DLP, DSPM, SaaS security, and Atlas AI security in the current platform.

Considerations

Access governance answers who could reach the data. Controlling what happens when that data actually moves is a separate layer. Watching data move is a dashboard; acting on it in runtime is security. Nightfall's design puts AI native detection first, so AI decides what is risky and the lineage you act on is the lineage that matters. Every incident arrives with a complete forensic story: who, role, data lineage, and prior behavior. Organizations with diverse SaaS portfolios also benefit from Nightfall's API first coverage, which extends the same policy across SaaS, endpoint, and agent surfaces without separate tooling per channel.

Best For: Organizations prioritizing access governance, permissions analytics, and least privilege enforcement for Microsoft 365 and file share environments, with growing AI security requirements.

5. Microsoft Purview

Microsoft Purview delivers native DLP capabilities within the Microsoft 365 ecosystem and, increasingly, beyond it.

Native Integration Advantages

• M365 Coverage: Deep integration with Exchange Online, SharePoint Online, OneDrive, Teams, and Microsoft 365 Copilot
• Unified Policies: Consistent policy management across supported Microsoft 365, endpoint, Copilot, and other documented Purview DLP locations
• Endpoint DLP: Native controls for supported Windows and macOS devices, subject to operating system, browser, onboarding, and licensing requirements
• Compliance Features: Built in compliance center with regulatory mappings
• AI Protection: Native controls for Microsoft Copilot plus AI security capabilities extending to third party AI applications

Licensing and Cost

As of July 2026, Microsoft lists Microsoft 365 E5 at approximately $60 per user per month with annual commitment in the United States, following an increase from $57 effective July 1, 2026. Purview licensing otherwise varies by base Microsoft 365 plan, workload, region, and add on, and E3 and Business Premium do not use identical eligibility or add on structures.

Considerations

Purview is strongest inside the Microsoft ecosystem, with coverage extending to non Microsoft cloud applications, browser DLP in Edge for Business, network data security for traffic to cloud and GenAI services, and enterprise integrations for third party AI assistants. Because it arrives bundled with existing licensing, organizations often adopt it as the default rather than as a deliberate selection, and as AI moves data autonomously, relying on a default posture becomes an active exposure rather than passive risk. Nightfall provides context aware detection across Microsoft 365 alongside every other surface, and a side by side view is available on the Nightfall vs Purview comparison page.

Best For: Microsoft centric enterprises seeking bundled DLP value within existing E5 investments, particularly those standardizing on Microsoft Copilot for AI assistance.

6. Netskope

Netskope provides DLP capabilities within its Security Service Edge (SSE) platform, combining inline and API based protection with a large cloud application catalog.

SSE Platform Capabilities

• Application Coverage: Visibility and risk intelligence across a very large catalog of cloud applications, including a substantial set of GenAI applications and tools
• Inline DLP: Inline content inspection for data in transit
• CASB Heritage: SaaS application visibility and control through managed cloud application connectors
• AI Security: Netskope One AI Security, announced by March 2026, spanning Shadow AI discovery, prompt and response controls, AI Gateway capabilities, and an agentic broker for governance of agentic and MCP communications
• SSE Integration: Unified with secure web gateway, ZTNA, and SSPM

Architecture Approach

Netskope uses traffic steering for inline inspection and also supports API based and out of band coverage for managed cloud applications. Available mechanisms include the Netskope Client, gateway and network steering options, reverse proxy and related inline modes, and API connectors. Coverage and enforcement behavior depend on the selected deployment mode.

Considerations

SSE is the right tool for web and sanctioned SaaS traffic, and Nightfall runs alongside it rather than replacing it. What sits outside a proxy path is the desktop agent runtime: the local stdio MCP server, the IDE embedded agent, the CLI, the desktop app, or the file on disk an agent just touched. That is the fastest growing exfiltration vector in the enterprise, and it is covered by Nightfall's endpoint DLP agent with the same detection brain used everywhere else. A structured breakdown is available on the Nightfall vs Netskope page.

Best For: Enterprises seeking unified SSE platforms combining DLP with secure web gateway, CASB, and zero trust network access capabilities.

7. Zscaler

Zscaler delivers DLP capabilities within its Zero Trust Exchange platform, providing cloud delivered data protection without traditional on premises DLP appliances.

Zero Trust Architecture

• Cloud Delivered: A Zero Trust Exchange enforcement architecture without traditional on premises DLP hardware, supplemented by endpoint, API, and client based controls
• ZIA/ZPA Integration: DLP enforcement through Zscaler Internet Access and Private Access
• Unified DLP Coverage: Controls spanning web, endpoints, email, SaaS, public cloud and IaaS, private applications, BYOD, GenAI, and agentic AI, including dedicated endpoint DLP
• AI and Agentic Security: AI security capabilities including file upload inspection, Shadow AI application discovery, AI asset and MCP server discovery, AI broker capabilities, and controls for agentic AI and MCP or A2A communications
• Scalable Architecture: Designed for distributed, cloud first organizations

Deployment Model

Zscaler's cloud first architecture removes on premises DLP appliance management. Organizations route traffic through the Zero Trust Exchange for inspection and policy enforcement, benefiting from centralized policy management and global enforcement points, while endpoint agents, API based SaaS scanning, and Client Connector extend coverage beyond inline web flows.

Considerations

Zscaler offers endpoint and agentic AI security capabilities, including MCP discovery and broker controls, so the useful question is one of architecture rather than presence. Gateway and broker layers route and observe traffic; the data layer decides what is sensitive and stops it. Nightfall classifies and enforces on the content itself across SaaS, endpoint, browser, email, and every agentic workflow, including the local surfaces that sit outside a proxy path. Nightfall complements an existing Zero Trust deployment, and the Nightfall vs Zscaler page sets out the coverage differences.

Best For: Organizations standardizing on Zscaler's Zero Trust Exchange for network security seeking integrated DLP within existing architecture investments.

Why Nightfall AI Stands Out for AI Data Security

Control First Architecture for AI Agent and MCP Data Movement

Nightfall's AI agent security offering is an enterprise DLP platform purpose built for MCP and agentic workflows. It covers local stdio and remote HTTP/SSE or Streamable HTTP protocols, with hooks for Cursor, Claude Code, and VS Code, and per server risk scoring for granular governance over AI agent data access. Nightfall scans and enforces policy on prompts, MCP tool calls, MCP tool responses, and shell commands.

Several established DLP, DSPM, and SSE vendors now market MCP and agentic AI controls, and the differentiator is implementation depth: native data movement enforcement, the specific MCP transports and IDE workflows supported, and whether the tool can act rather than only observe. Architectures built around network paths or data at rest sit outside local stdio MCP activity and IDE embedded tool calls without endpoint level telemetry, which is precisely the gap Nightfall's endpoint native approach closes. Further context is available in the guide to MCP security for CISOs.

AI Native Detection With Less Tuning Overhead

The platform combines AI based models, LLM file classifiers, and computer vision, drawing on more than 100 AI and machine learning models. Pretrained detectors and default policies remove the lengthy tuning cycles associated with regex based DLP, while customers can still tailor rules and build custom detectors for their environments, as Snyk did. Nightfall cuts false positives by 95% relative to traditional DLP, which translates directly into lower alert volume, less alert fatigue, and less manual investigation work. SecOps moves from triage to oversight and governance.

Fast Time to Value

An API first architecture enables OAuth integration setup in under one hour per SaaS application, with no network changes and no agents required for SaaS API scanning. Endpoint rollout begins in approximately 30 minutes through MDM tools such as Jamf or Intune. An initial scan reveals blind spots within 24 hours, full endpoint fleet coverage lands within a week, and comprehensive protection is in place in under a month, with a typical proof of concept completed in roughly two weeks. Discovery and posture are a byproduct of prevention rather than a precondition for it.

Real Time Control, Not Just Visibility

Nightfall's control first approach enforces through blocking, coaching, override workflows, and automated remediation. The platform shifts data exfiltration prevention from reactive incident response toward proactive protection. Real time user coaching via Slack, Microsoft Teams, and email creates a human firewall, and 80% of incidents are resolved through a combination of automated remediation and employee self remediation, building security culture while reducing operational burden. Seeing the leak is not the win. Stopping it is.

Unified Platform That Reduces Vendor Sprawl

Organizations consolidate DLP, insider risk, and AI governance into a single stack rather than managing separate tools for email DLP, endpoint DLP, SaaS monitoring, insider risk detection, and GenAI security. That collapses three contracts, three vendor relationships, and three budget lines into one, reduces policy fragmentation and integration overhead, and enables faster investigations through unified context and forensic search across all data movement, not just policy violations.

Reported Enterprise Results

More than 100 organizations use Nightfall, including Gusto, DraftKings, Grafana Labs, Grab, Nubank, and Decagon, with 20x average ROI and documented customer outcomes spanning fintech, healthcare, software, and AI native companies. For security teams evaluating alternatives, Nightfall's combination of AI native detection, real time enforcement, and endpoint native agent coverage makes it the control layer for the AI era. Request a demo to see how Nightfall governs data movement across human and AI workflows in your environment.

Frequently Asked Questions

What are the main differences between Securiti AI and Nightfall AI?

Securiti, now Veeam's Securiti AI, has broader roots in privacy operations, DSR automation, DSPM, and governance, and also markets data security, AI security and governance, Agent Commander, LLM firewalls, access enforcement, and automated remediation. Nightfall is differentiated by its control first architecture and native data movement enforcement across SaaS, email, endpoints, browsers, MCP servers, and AI applications, for both human and agent actors in real time. The comparison comes down to enforcement depth, supported channels, policy granularity, deployment model, and operational overhead. A wider view is available on the Nightfall comparison hub.

How does Nightfall AI protect against Shadow AI and GenAI data leakage?

Nightfall provides browser plugins and endpoint agents that monitor AI interactions in real time across ChatGPT, Microsoft Copilot, Claude, Gemini, DeepSeek, Perplexity, and Grok. It inspects prompts, file uploads, and clipboard content, and supports prompt sanitization or redaction before submission, covering secrets, credentials, PHI, PCI, and PII. This enables organizations to prevent data leakage to Shadow AI while still enabling AI adoption.

Can organizations run Securiti AI and Nightfall AI together?

Yes. They work well as complementary platforms when an organization wants separate privacy operations and data movement control layers. Securiti has deep roots in privacy automation, compliance workflows, and data estate mapping, while Nightfall provides real time DLP enforcement and agentic data control across every surface.

How quickly can Nightfall AI deploy compared to enterprise DSPM solutions?

SaaS applications are connected in minutes and generally configured in under an hour, with an initial scan identifying exposures within 24 hours, full endpoint fleet coverage within a week, and comprehensive protection in under a month. DSPM deployment timelines vary with data source count, permissions, network topology, data volume, scan depth, and remediation scope, which is why Nightfall treats discovery as a byproduct of prevention rather than a prerequisite.

What AI agent and MCP security capabilities does Nightfall AI provide?

Nightfall covers local stdio and remote HTTP/SSE MCP workflows, provides IDE hooks for coding assistants, and supports risk scoring and tool classification for read, read/write, and destructive actions, with prompt injection detection on agent traffic and full inline blocking. It scans and enforces policy on prompts, MCP tool calls, tool responses, and shell commands. Other vendors market MCP or agentic controls as well, and the practical differences show up in transports covered, interception points, policy actions, and runtime enforcement. See how MCP bypasses traditional security tools for background.

What detection precision does Nightfall AI report?

Nightfall delivers 95% precision out of the box against a 5% to 25% legacy DLP baseline. It uses ML detectors for PII, PHI, secrets, credentials, and financial data, LLM classifiers across more than 20 file categories, and computer vision for images and screenshots. Detectors are customer trainable and auto retraining, so precision improves with use. Higher precision means fewer false positives, less alert fatigue, and materially less manual investigation work. Details on packaging are on the Nightfall pricing page.