Best DLP Solutions for CMMC Compliance in 2026

Defense contractors face a new reality in 2026: CMMC Phase I enforcement began in November 2025, introducing assessment requirements for applicable DoD contracts. Phase II requirements were suspended in July 2026, while Phase I self-assessment requirements remain in place. At the same time, AI agents, copilots, and MCP servers are moving sensitive data at machine speed through increasingly complex enterprise workflows.

This guide compares data loss prevention (DLP) solutions based on CMMC alignment, CUI protection capabilities, GenAI monitoring, detection accuracy, deployment fit, and enterprise relevance for defense contractors.

Key Takeaways

• CMMC requirements: Phase I self-assessment requirements remain in place, while Phase II requirements were suspended in July 2026. Organizations need appropriate DLP controls to help safeguard CUI across modern workflows.
• DLP and encryption: These controls serve different purposes. Organizations need appropriate data movement controls and FIPS-validated cryptographic mechanisms where required.
• AI agent security: AI agents introduce additional CUI exposure paths through MCP tool calls, coding assistants, and autonomous workflows. AI-native security helps organizations govern sensitive data movement across these environments.
• Nightfall's approach: Nightfall provides AI-native detection, real-time prevention, and policy enforcement across supported SaaS, email, browsers, endpoints, and AI workflows.
• MCP governance: Monitoring agent activity, controlling tool access, and applying data policies across MCP workflows are important considerations when AI agents handle sensitive information.
• Detection and response: Context-aware classification, automated remediation, and consistent policy enforcement help security teams manage sensitive data exposure and reduce manual investigation.

1. Nightfall AI

Nightfall is the AI data security platform built to control AI agents and all data they touch. Its core message is simple: AI moves your data. Nightfall controls it. Nightfall helps organizations adopt AI while enforcing data boundaries across humans, copilots, AI agents, SaaS applications, email, browsers, endpoints, and MCP workflows.

Nightfall is designed for the way data moves today. Employees paste customer information into AI tools. Developers connect coding agents to repositories. MCP servers expose tools and data to agents. SaaS files are shared, copied, renamed, synced, and moved across destinations. Nightfall brings these movements into one AI-native control layer.

Core Capabilities

• AI-native detection using machine learning and contextual classification.
• MCP security for AI agent activity, granular access controls, and sensitive data exposure prevention.
• ML detectors for PII, PHI, secrets, credentials, and financial data, alongside LLM-based classification.
• Real-time controls including blocking, coaching, redaction, deletion, revocation, quarantine, encryption, and automated remediation, depending on the supported workflow.
• Coverage across SaaS, endpoints, email, browsers, AI applications, AI agents, and MCP workflows.
• API-based SaaS integrations designed for rapid deployment.
• AI-native investigation capabilities including data lineage, policy recommendations, and incident analysis.

Strengths

• Unified data movement control: Nightfall addresses the core challenge of AI-era data security by controlling how data moves and who or what is moving it across humans, AI agents, copilots, MCP servers, browsers, endpoints, SaaS apps, and email workflows.
• AI-native classification: Nightfall goes beyond sensitive data detection by applying AI-native classification, contextual analysis, and data lineage tracking to identify potentially risky data movement.
• Real-time policy enforcement: Nightfall enforces policies in real time to help prevent unauthorized CUI exposure through automated workflows with limited direct human involvement.
• Reliable detection: "Nightfall is reliable. When it says there's a detection, we trust that detection. For people in my field, that's a big factor. You don't want to waste time chasing ghosts," says Victor Sogaolu, Staff Security Engineer at Snyk.
• AI-era data protection: For defense contractors seeking to improve detection quality while enabling AI adoption, Nightfall provides an AI-native approach to data protection, combining detection and policy enforcement across supported data movement surfaces.

CMMC Alignment

Nightfall's data lineage, logging, and automated policy enforcement capabilities may support selected CMMC security requirements, including requirements associated with auditability, access governance, and incident investigation.

Its real-time sensitive data controls and supported encryption-related remediation actions can contribute to a broader CUI security program. Organizations must still implement applicable CMMC requirements, including validated cryptographic protection where required.

2. Kiteworks Private Data Network

Kiteworks provides a Private Data Network designed for secure information exchange, including workflows involving CUI. The platform consolidates secure file sharing, managed file transfer (MFT), SFTP, and email under a centralized governance framework.

Core Capabilities

• FIPS 140-3 validated cryptographic capabilities in applicable products and configurations.
• Audit logging and chain-of-custody tracking.
• Access controls with MFA and SSO support.
• Secure file sharing, managed file transfer, SFTP, and email.
• On-premises and private cloud deployment options.

Kiteworks' Primary Focus

Kiteworks focuses on securing structured information exchange between organizations and their external partners. Its file transfer, access control, and audit capabilities can support workflows involving sensitive documents shared across organizational boundaries.

For defense contractors exchanging CUI with subcontractors and supply chain partners, centralized transfer policies and auditable access records can contribute to information security processes.

CMMC Alignment

Kiteworks provides encryption, access control, and audit capabilities that can support selected CMMC Level 2 requirements, including requirements relating to system communications and accountability.

The extent of that alignment depends on the deployed capabilities and implementation.

Coverage Considerations

Kiteworks' secure exchange controls cover supported data-transfer workflows, with applicability determined by deployed products and configurations.

Nightfall Differentiation

Nightfall complements secure exchange platforms by providing visibility and control across modern data movement surfaces, including SaaS applications, AI tools, browsers, endpoints, and MCP workflows.

Its AI-native detection and real-time enforcement help organizations govern sensitive information as humans and AI agents interact with productivity tools and enterprise data.

3. Microsoft Purview DLP

Microsoft Purview DLP provides data loss prevention capabilities across the Microsoft 365 ecosystem, including Exchange, SharePoint, OneDrive, and Teams. It also supports endpoint data protection and selected AI applications and integrations.

Core Capabilities

• Native DLP across Exchange, SharePoint, OneDrive, and Teams.
• Sensitivity labels and classification capabilities.
• Regulatory policy templates for common compliance scenarios.
• Endpoint DLP for supported Windows and macOS devices.
• Data security and governance capabilities for Microsoft 365 Copilot and supported AI applications.

Microsoft Purview's Primary Focus

Microsoft Purview brings data classification, information protection, and governance into Microsoft 365 environments.

Organizations using Microsoft infrastructure can apply DLP policies across supported collaboration services, endpoints, and AI interactions using Purview's policy management capabilities.

CMMC Alignment

Purview's classification, access management, monitoring, and auditing capabilities may support selected NIST SP 800-171 requirements when appropriately configured.

Its contribution depends on the Microsoft services, endpoints, and integrations included in the organization's deployment.

Coverage Considerations

Purview's coverage and enforcement mechanisms vary by application, device platform, integration, and enabled capabilities, including the configured Microsoft security scope.

Nightfall Differentiation

Nightfall provides AI-native detection and real-time data control across supported Microsoft and non-Microsoft environments. Its coverage extends to shadow AI activity, coding assistants, local MCP connections, and supported agentic workflows.

Nightfall brings these capabilities together with SaaS and endpoint data security through a unified detection and policy enforcement approach, as further detailed in its Microsoft Purview comparison.

4. CrowdStrike Falcon Data Protection

CrowdStrike Falcon Data Protection provides sensitive data monitoring and enforcement capabilities within the Falcon platform. CrowdStrike's broader data security offerings cover endpoints, browsers, SaaS, cloud environments, and GenAI workflows.

Core Capabilities

• Endpoint monitoring for sensitive data movement.
• Controls for supported file operations, removable media, and uploads.
• Data classification and policy enforcement.
• Integration with Falcon endpoint detection and response capabilities.
• Data security capabilities spanning supported SaaS, cloud, and AI workflows.

CrowdStrike's Primary Focus

CrowdStrike integrates data security with its broader endpoint and threat detection ecosystem.

Its platform brings together endpoint telemetry, data movement context, and threat investigation capabilities to support security operations.

CMMC Alignment

Falcon's monitoring, threat detection, and incident investigation capabilities can support selected CMMC requirements relating to auditability and incident response.

Specific data protection coverage depends on the Falcon products and modules deployed.

Coverage Considerations

Falcon data protection coverage depends on the products and modules deployed and their relevance to applicable CUI, cryptographic, and media protection requirements.

Nightfall Differentiation

Nightfall provides an AI-native data security control layer across supported SaaS, endpoint, and agentic workflows.

It brings contextual detection, sensitive data classification, MCP governance, and real-time policy enforcement into a unified approach focused on controlling data movement by both people and AI agents.

Nightfall can complement existing endpoint detection and response infrastructure without requiring organizations to replace their broader security platforms.

5. Symantec DLP (Broadcom)

Symantec DLP provides enterprise data loss prevention across network, endpoint, storage, email, and cloud channels. The platform includes content inspection, classification, and policy enforcement capabilities for regulated environments.

Core Capabilities

• DLP coverage across network, endpoint, storage, email, and cloud.
• Content inspection for supported browser and web activities.
• Exact Data Matching (EDM), OCR, and pattern matching.
• Network traffic inspection.
• Centralized policy management and incident reporting.

Symantec's Primary Focus

Symantec focuses on identifying and controlling sensitive data through enterprise DLP policies.

Its classification and content inspection capabilities support organizations that need to govern information movement across multiple infrastructure and communication channels.

CMMC Alignment

Symantec's data inspection, monitoring, and incident reporting capabilities may support selected CMMC requirements involving access control, auditability, and sensitive information handling.

Coverage Considerations

Symantec's contribution to CMMC cryptographic requirements depends on the encryption mechanisms, validation status, and deployment configurations used to safeguard CUI.

Nightfall Differentiation

Nightfall emphasizes AI-native detection and real-time control across modern data movement workflows.

Its integrated coverage of supported AI agents, MCP connections, coding assistants, SaaS applications, and endpoints helps security teams apply contextual classification and enforcement to sensitive data handled by humans and automated systems.

Nightfall also supports rapid SaaS integration deployment and automated remediation workflows.

6. Forcepoint DLP

Forcepoint DLP uses content analysis and risk-adaptive security capabilities to help organizations manage sensitive data movement and insider risk. Its broader data security portfolio includes behavioral analytics and policy enforcement across supported channels.

Core Capabilities

• Risk-Adaptive Protection using contextual and behavioral indicators.
• AI-related security capabilities for supported GenAI workflows.
• Dynamic policy enforcement based on risk context.
• User behavior analytics.
• DLP policies for supported endpoint, web, email, and network channels.

Forcepoint's Primary Focus

Forcepoint focuses on understanding the context surrounding data movement, including user behavior and potential insider risk.

Its risk-adaptive approach can adjust protection measures based on configured security policies and activity indicators.

CMMC Alignment

Forcepoint's DLP and behavioral monitoring capabilities can support selected security requirements involving access control, monitoring, and incident investigation.

Its email security portfolio also documents encryption capabilities that can be incorporated into relevant workflows.

Coverage Considerations

Forcepoint's contribution to applicable CMMC requirements depends on its configured DLP, email encryption, and other security capabilities.

Nightfall Differentiation

Nightfall combines context-aware detection with AI-native classification and real-time policy enforcement. The Nightfall vs. Forcepoint comparison highlights Nightfall's approach to sensitive data protection across human and AI-driven workflows.

Its security coverage includes supported AI agent activity, MCP server connections, and coding assistant workflows, enabling organizations to apply sensitive data controls to both human-driven and agent-driven actions.

7. Proofpoint Enterprise DLP

Proofpoint Enterprise DLP provides data loss prevention capabilities across supported email, cloud, and endpoint environments. Its security portfolio also includes human risk analysis and AI security capabilities.

Core Capabilities

• Email DLP with content inspection and policy enforcement.
• Compliance-oriented classification and policy templates.
• Human Risk Explorer for user risk analysis.
• Integrations with supported Microsoft 365 and Google Workspace environments.
• AI security and agent governance capabilities expanded through its acquisition of Acuvity in February 2026.

Proofpoint's Primary Focus

Proofpoint emphasizes data security in employee communication and collaboration workflows, supported by user risk context and security analytics.

Its AI security offerings also address supported AI interactions, agent behavior, and runtime enforcement.

CMMC Alignment

Proofpoint's monitoring, content inspection, and policy management capabilities can contribute to selected CMMC security requirements.

Relevant capabilities depend on the deployed products, integrations, and configuration.

Coverage Considerations

Proofpoint's contribution to CUI protection and cryptographic requirements depends on the relevant deployment scope, products, integrations, and configuration.

Nightfall Differentiation

Nightfall brings AI-native sensitive data detection, real-time prevention, and cross-surface policy enforcement into its AI data security platform.

Its supported capabilities include SaaS data controls, endpoints, coding assistants, local and remote MCP workflows, and AI agent activity. This gives security teams a unified approach to detecting and controlling sensitive data movement across human and agentic workflows.

8. Netskope One DLP

Netskope One DLP provides data loss prevention capabilities within the Netskope One security platform. It supports sensitive data inspection and policy enforcement across cloud services, websites, endpoints, and AI-related workflows.

Core Capabilities

• Cloud-native DLP architecture.
• Integration with CASB and SaaS security capabilities.
• Security service edge and Zero Trust Network Access features.
• SaaS and shadow IT discovery.
• Endpoint data protection controls.
• AI and MCP-related security capabilities through supported platform components.

Netskope's Primary Focus

Netskope focuses on securing access and data movement across cloud applications, web environments, and connected enterprise resources.

Its DLP capabilities work alongside broader access security controls, providing policy enforcement across supported traffic and endpoint activities.

CMMC Alignment

Netskope's monitoring, access control, and data protection capabilities may support selected CMMC requirements involving access control and system communications.

Coverage Considerations

Coverage of endpoint activities, SaaS data, AI interactions, MCP communications, and CUI movement depends on the platform components and policies deployed.

Nightfall Differentiation

Nightfall provides AI-native data detection and enforcement directly across supported application, endpoint, and agentic workflows.

Its focus includes sensitive content within local MCP activity, IDE-embedded agents, and supported AI tool interactions. Nightfall can work alongside existing secure access infrastructure while providing a unified data security control layer.

9. Trellix DLP

Trellix DLP provides data loss prevention across supported endpoint, email, web, network, and cloud workflows. Its portfolio includes data security management, encryption products, and AI-related risk monitoring capabilities.

Core Capabilities

• DLP across supported endpoints, email, web, and network channels.
• AI Data Risk Dashboard for identifying and addressing AI-related data exposure.
• Integration with broader threat detection and response workflows.
• User coaching and policy-based controls.
• Separate encryption capabilities for enterprise devices and removable media.

Trellix's Primary Focus

Trellix provides centralized data security controls and incident investigation capabilities within its security portfolio.

Its AI Data Risk Dashboard supports monitoring and enforcement of configured data security policies for supported AI usage, including uploads and clipboard interactions.

CMMC Alignment

Trellix's DLP, incident monitoring, and encryption products may contribute to selected CMMC requirements relating to auditability, media protection, and system security.

Coverage Considerations

Trellix's contribution to applicable CMMC requirements depends on the selected DLP and encryption capabilities, cryptographic modules, deployment settings, and configuration.

Nightfall Differentiation

Nightfall provides AI-native detection and real-time enforcement across supported AI agent, MCP, endpoint, and SaaS workflows.

Its contextual classification capabilities help security teams identify sensitive information as humans and agents interact with enterprise tools. Unified policy enforcement also supports consistent handling of sensitive data across these environments.

10. Digital Guardian (Fortra)

Digital Guardian provides data loss prevention capabilities for intellectual property, regulated information, and other sensitive enterprise data. Its endpoint-focused architecture supports monitoring and enforcement across managed devices and supported data movement channels.

Core Capabilities

• Endpoint agents supporting Windows, macOS, and Linux environments.
• Data classification and content inspection.
• Database Record Matching (DBRM).
• Support for sensitive file types, including documents and source code.
• SaaS, on-premises, managed services, and hybrid deployment options.

Digital Guardian's Primary Focus

Digital Guardian focuses on controlling sensitive data usage and movement across enterprise endpoints.

Its data discovery and classification capabilities support intellectual property protection and other regulated information security use cases, including technical documents and engineering-related information.

CMMC Alignment

Digital Guardian's endpoint monitoring, classification, and policy enforcement capabilities may support selected requirements involving information access, auditability, and media protection.

Coverage Considerations

The applicability of Digital Guardian and other Fortra components to specific CMMC requirements depends on deployment scope, configured capabilities, and relevant cryptographic validation.

Nightfall Differentiation

Nightfall extends sensitive data protection into supported collaboration and AI-driven workflows.

Its AI-native controls govern data movement through SaaS applications, coding assistants, endpoints, and MCP server connections. This supports real-time decisions about sensitive data as employees and AI agents access, process, and transfer information.

Understanding CMMC Compliance Requirements in an AI-Driven World

The Cybersecurity Maturity Model Certification (CMMC) framework requires applicable defense contractors to safeguard controlled unclassified information (CUI). CMMC Level 2 aligns with NIST SP 800-171 Revision 2, establishing 110 security requirements across 14 domains, including Access Control (AC), Audit and Accountability (AU), Media Protection (MP), and System and Communications Protection (SC).

For DLP selection, relevant CMMC requirements include:

• SC.L2-3.13.11: Employ FIPS-validated cryptography when used to protect CUI confidentiality.
• AU.L2-3.3.8: Protect audit information and audit logging tools from unauthorized access, modification, and deletion.
• AC.L2-3.1.5: Employ the principle of least privilege, including for security functions and privileged accounts.
• MP.L2-3.8.7: Control the use of removable media on system components.

These requirements address different aspects of CUI protection. DLP can support data monitoring and enforcement, while encryption, access management, audit controls, and other safeguards address additional requirements.

The Defense Industrial Base (DIB) operates in an environment where CUI can move through SaaS applications, AI coding assistants, browser uploads, endpoint file transfers, and MCP server connections. Organizations therefore need to account for data movement across both human-driven activities and automated AI workflows.

Why AI Workflows Change DLP Requirements for CMMC

DLP technologies have evolved around monitoring content and enforcing policies across communication and storage channels. AI agents, copilots, and MCP servers introduce additional ways to access, transform, and move sensitive information.

Security teams need data controls capable of addressing these workflows alongside established endpoint, email, SaaS, and network protection measures.

Traditional Rule-Based DLP Considerations

Rule-based DLP approaches commonly use patterns, predefined conditions, and policy matching to classify sensitive data.

Relevant evaluation factors include:

• Alert quality: How policy configuration influences alert relevance and investigation workflows.
• Context awareness: Whether classification uses content meaning and activity context.
• AI workflow coverage: Support for coding assistants, MCP traffic, and agent actions.
• Deployment requirements: The configuration and integration steps needed for relevant environments.
• Policy enforcement: The ability to apply preventive controls to supported data movement events.

Covering AI-Generated and AI-Moved Data

AI agents can query, retrieve, transform, and route CUI across connected tools with less direct human involvement.

For example, a developer using a coding assistant connected to a repository might inadvertently expose customer data or credentials. An employee pasting contract terms into ChatGPT could disclose CUI to an external service. An MCP server connection could give an AI agent access to sensitive databases.

Security teams need solutions that can inspect relevant content and enforce data policies across the surfaces where CUI moves.

Rapid Deployment and Operational Benefits of Modern DLP for CMMC

Defense contractors face pressure to implement relevant security controls while maintaining operational productivity. Modern AI-native DLP platforms offer API-based integrations, data discovery and classification, and policy enforcement capabilities that can simplify data protection operations.

Deployment Considerations

• Endpoint deployments: Agents can be distributed through supported device management systems.
• Cloud-native platforms: API-based SaaS integrations can simplify access to relevant application data.
• Nightfall deployment: Nightfall supports SaaS integrations and endpoint deployment through managed workflows.

Operational Burden Reduction

• Detection accuracy: Context-aware classification can help security teams prioritize relevant findings.
• Automated remediation: Nightfall supports automated remediation and employee self-remediation workflows.
• Consolidated management: A unified approach to data security, insider risk, and AI governance can reduce the need to manage disconnected policies.

For defense contractors balancing CMMC compliance with AI adoption, these capabilities can support more consistent enforcement of relevant data protection controls.

Faster deployment can accelerate the implementation of safeguards, while contextual detection helps security teams focus on sensitive data movement requiring attention.

Selecting the Right DLP Solution for CMMC: Key Considerations in 2026

Selecting a DLP solution for CMMC compliance requires evaluating several factors beyond feature checklists. The goal is to identify controls that address the organization's CUI environment, support applicable security requirements, and integrate with established security operations.

Nightfall addresses these priorities through AI-native sensitive data detection, real-time policy enforcement, and unified control across supported SaaS applications, endpoints, AI agents, and MCP workflows.

CMMC-Specific Criteria

• NIST SP 800-171 control mapping: Does the solution provide capabilities relevant to applicable CMMC Level 2 requirements?
• CUI encryption: Are appropriate FIPS-validated cryptographic mechanisms implemented where required?
• Audit logging: Does the deployment provide appropriate logging and protection for audit information?
• External cloud services: Are applicable cloud service security obligations under relevant contractual requirements addressed?

AI-Readiness Criteria

• GenAI monitoring: Can the platform inspect and control supported sensitive data movement to AI tools?
• MCP security: Does the platform govern AI agent connections and MCP tool activity?
• Coding assistant coverage: Can the platform monitor supported IDE-embedded agents such as Cursor, VS Code, and Claude Code?
• Shadow AI discovery: Can the platform identify relevant unsanctioned AI tool usage?

Operational Criteria

• Detection accuracy: How effectively does the platform distinguish sensitive data movement from routine activity?
• Deployment timeline: How quickly can relevant controls become operational?
• Operational requirements: What ongoing management, policy maintenance, and investigation processes are necessary?
• Integration ecosystem: Does the platform integrate with existing SIEM, SOAR, and ITSM tools?

Why Choose Nightfall AI for AI-Era CMMC Compliance

AI-Native Sensitive Data Detection

Nightfall uses context-aware classification to identify sensitive information, including PII, PHI, credentials, and financial data. Its AI-native detection helps security teams distinguish legitimate business activity from potentially risky data movement, supporting more accurate identification and investigation of sensitive data exposure.

AI Agent and MCP Security

Nightfall provides native MCP security, coding assistant monitoring, and GenAI governance capabilities to help control sensitive data movement through supported agentic workflows. These controls help organizations govern how AI agents access, process, and transfer sensitive information across connected tools and enterprise environments.

Real-Time Policy Enforcement

Through AI-native detection, unified policies, data lineage, and automated remediation, Nightfall helps security teams identify risks and act on sensitive data movement. Its real-time enforcement capabilities include blocking, coaching, and other policy-based responses to help prevent unauthorized data exposure.

Unified Data Security Across Workflows

Nightfall brings detection, investigation, and policy enforcement together across supported SaaS applications, endpoints, browsers, email, AI tools, and MCP workflows. This unified approach helps organizations maintain consistent data security policies as sensitive information moves between employees, applications, and AI agents.

Streamlined Deployment and Security Operations

Nightfall supports API-based SaaS integrations, managed endpoint deployment, and automated remediation workflows. Its contextual investigation capabilities help security teams prioritize relevant risks, reduce manual investigation, and maintain effective data security operations.

For defense contractors implementing CMMC security requirements while enabling AI adoption, Nightfall provides a unified AI data security platform, helping organizations enforce appropriate data boundaries across human and agentic workflows as part of a broader compliance program.

Frequently Asked Questions

What specific CMMC controls can a modern DLP solution support?

Modern DLP solutions may support selected CMMC Level 2 requirements associated with Access Control (AC), Audit and Accountability (AU), Media Protection (MP), and System and Communications Protection (SC). The level of support depends on the specific controls and deployment. For example, DLP policies may help restrict unauthorized data transfers, while logs and policy events can contribute to monitoring and investigation. Cryptographic requirements such as SC.L2-3.13.11 require the use of appropriate validated cryptographic mechanisms where applicable.

How can a DLP solution distinguish between legitimate business activity and data exfiltration by AI agents?

AI-native DLP platforms can use machine learning, contextual classification, risk scoring, and data lineage to evaluate sensitive data movement. Nightfall applies AI-native detection and agent activity context to identify potentially risky actions and enforce configured policies. Its capabilities support security teams in distinguishing authorized workflows from sensitive data exposure that requires investigation or prevention.

What are the deployment timelines and operational impacts of implementing DLP for CMMC compliance?

Deployment timelines vary by platform architecture, integration requirements, and the organization's environment. Cloud-native solutions may support API-based connections to SaaS applications, while endpoint controls generally require device deployment and policy configuration. Nightfall supports managed endpoint deployment and SaaS integrations designed to simplify implementation. Organizations must still configure applicable policies, establish monitoring procedures, and document the controls relevant to their CMMC obligations.

How does a modern DLP solution handle sensitive data moving through AI-powered coding assistants and copilots?

Modern AI data security platforms can provide coverage for supported coding assistants, IDE-embedded agents, and MCP server connections. Nightfall supports local and remote MCP workflows, IDE hooks, sensitive content inspection, risk scoring, and policy enforcement across applicable agent activity. These controls help organizations govern how coding assistants and AI agents access or transfer sensitive information.

Can a single DLP platform consolidate insider risk, AI governance, and traditional data security for CMMC?

Modern AI data security platforms can bring DLP, insider risk, and AI governance capabilities together within one control layer. Nightfall provides sensitive data detection, real-time enforcement, investigation context, and AI governance capabilities across supported SaaS, endpoints, browsers, email, AI tools, and MCP workflows. This consolidated approach can simplify policy management and security operations. Organizations must still implement additional safeguards required by their applicable CMMC obligations.