Best AI Agent Security Platforms for Securing Claude Enterprise in 2026

Claude Enterprise gives organizations access to agentic capabilities such as Claude Code and Claude Cowork that can perform multi-step tasks and interact with authorized files, applications, connectors, and tools, subject to permissions. That capability expands where sensitive data moves and changes which control points matter. The risk is not theoretical: Gravitee's December 2025 survey found that 88% of organizations reported confirmed or suspected AI agent security or data privacy incidents, while Gravitee's April 2026 survey wave reported 54% experiencing or suspecting such an incident during the prior 12 months, including 34.9% reporting a confirmed incident.

Most security tools were built for either human-driven data movement or individual AI applications. Agents present a new challenge, autonomously accessing, transforming, and moving data across enterprise environments, so coverage of AI agent execution paths now varies by vendor and deployment architecture. Anthropic itself now lists Claude Compliance API integrations with established security vendors. The choice of AI agent and MCP security architecture materially affects an organization's visibility, preventive controls, governance, and incident response capabilities around Claude. This guide examines seven platforms that address Claude Enterprise security needs in 2026, starting with Nightfall AI, the AI security platform built to control AI agents and all the data they touch.

Key Takeaways

Unified platforms reduce tool sprawl: Solutions that combine data loss prevention with AI agent security consolidate insider risk and AI governance into one stack, which means one platform and one contract instead of several, and keep policy consistent across human and AI activity
AI-native detection changes the false positive economics: Nightfall reports up to 95% detection precision out of the box, evidenced during a proof of value on customer data, and a 99% reduction in false positives from its AI-powered detection platform. Incumbent platforms such as Microsoft Purview and Palo Alto Networks Enterprise DLP document trainable ML classifiers and ML or LLM enhanced patterns, while Nightfall applies the same AI-native detection across SaaS, endpoints, browsers, email, and agentic surfaces
MCP security should account for stdio and Streamable HTTP: The MCP specification defines stdio, which is local process-to-process communication, and Streamable HTTP, the standardized network transport. Local stdio activity sits outside the view of controls that inspect only network traffic, and Nightfall covers local stdio and remote MCP transports with the same detection brain. Remote Cowork sessions do not run local MCP servers, so control points differ between local and remote execution
Inline enforcement and telemetry work together: Nightfall pairs full inline blocking with continuous telemetry, so prevention and investigation run on the same data detection and response engine. Anthropic's own enforcement path, Inference Hooks, is in beta, while Compliance API and OpenTelemetry monitoring carry the visibility load across Claude, Claude Code, and Cowork
Lightweight deployment accelerates time to value: Nightfall deploys in minutes, with roughly 10 minutes to connect a first SaaS app or deploy the endpoint agent to hundreds of users, endpoint rollout via MDM in approximately 30 minutes, and most teams protected the same day
Autonomous investigation reduces SecOps burden: Nyx, Nightfall's agentic DLP analyst, investigates incidents around the clock and resolves routine cases, shifting security teams from manual triage toward higher-value investigation and governance

1. Nightfall AI

Nightfall AI is the AI security platform built to control AI agents and all the data they touch. AI agents now move data autonomously at machine speed, and Nightfall controls that movement in real time with comprehensive coverage across endpoints, MCP servers, email, browsers, and SaaS. One platform replaces the separate tools organizations otherwise run for traditional DLP, insider risk, and AI agent security. On June 11, 2026, Nightfall launched AI Agent Security, bringing real-time control to autonomous AI workflows, backed by the same detection brain that already governs data exfiltration prevention across the rest of the estate.

Nightfall was co-founded by Rohan Sathe, a founding engineer at Uber Eats, and is backed by Bain Capital Ventures, Venrock, WestBridge Capital, Webb Investment Network, and Pear VC, along with cybersecurity leaders Kevin Mandia, Freddy Kerrest, and Doug Merritt. The company emerged from stealth with its Series A and later raised a $40 million Series B to expand its data protection platform. Hundreds of organizations run on Nightfall, including Sierra AI, Legora, Mercado Libre, Nubank, Rackspace, and DraftKings, and its customers include 10% of the Forbes AI 50.

How Does Nightfall AI Work?

Nightfall's platform delivers real-time visibility and control over sensitive data movement by humans and AI agents. Key capabilities include:

AI-Native Detection: 100+ ML-based detectors and LLM classifiers across 20+ categories covering PII, PHI, PCI, secrets, credentials, and source code, built on context-aware detection rather than regex, and customer-trainable with auto-retraining. Nightfall reports up to 95% detection precision, evidenced during a proof of value on customer data
Claude Coverage by Surface: A live Claude Compliance API integration that Anthropic lists as a Claude Compliance API security integration, plus hooks-based inspection and enforcement for Claude Code prompts, MCP tool calls, tool responses, and shell commands, an OpenTelemetry audit trail for Claude Cowork sessions covering cost, tokens, and tool invocations, and Compliance API monitoring of Claude Enterprise conversations, files, projects, and activity feed
MCP Security: Discovery across local stdio and remote transports, per-server risk scoring, tool and capability classification by what each tool can do, whether read, read/write, or destructive, and real-time enforcement on MCP tool calls and tool responses, with prompt injection detection and prevention on agent traffic
Real-Time Remediation: Block, coach, redact, delete, revoke, quarantine, and encrypt actions across supported integrations and policy types, with manual or automated approval workflows and multi-channel delivery through Slack, Teams, email, Jira, and on-device notifications. Inline controls prevent or modify a transaction before it completes
Autonomous Investigation: Nyx, Nightfall's agentic DLP analyst, provides 24/7 incident investigation, policy recommendations, and risky user surfacing

Documented Results

Nightfall's published, first-party results include:

20x average ROI, with an ROI calculator that models return against reduced manual investigation time
Four in five incidents, or 80%, resolved through automation or employee self-remediation, supported by governance and risk workflows
Detection precision of up to 95% out of the box, alongside a 99% reduction in false positives, so security teams spend their time on true positives rather than noise
SaaS integration deployment in minutes to under one hour, with roughly 10 minutes to connect a first SaaS app or deploy the endpoint agent to hundreds of users, and most teams protected the same day
Endpoint agent deployment in approximately 30 minutes via MDM, with MCP and AI agent coverage rolling out alongside it

Platform Architecture

Nightfall runs one detection brain across every surface, applying consistent AI-native classification whether data moves through Slack, Gmail, endpoints, browsers, or AI agent workflows. Because prevention comes first, posture and data discovery and classification arrive as a byproduct rather than as a prerequisite that has to be completed before protection begins. The platform captures continuous telemetry across data-movement channels, not just policy violations, with investigation context including HRIS and IdP metadata, session replay, and endpoint lineage.

The single endpoint agent covers both human and AI or MCP traffic across 10+ vectors at approximately 1% CPU and 50MB RAM, with macOS and Windows parity. Browser security covers Chrome, Edge, Firefox, Safari, Arc, Brave, Comet, Atlas, and Vivaldi, including the AI-native browsers that Nightfall's detection engine inspects natively.

Best For: Organizations that want one platform for traditional DLP, insider risk, and AI agent security, with AI-native detection, real-time inline control, and Claude coverage spanning the Compliance API, Claude Code hooks, Cowork telemetry, and MCP stdio and remote transports.

2. Zenity

Zenity focuses on AI agent governance at enterprise scale and now offers a direct Claude Enterprise integration. On June 12, 2026, Zenity announced an integration with the Claude Compliance API, described further in Zenity's product write-up of the integration, and Anthropic lists Zenity among its Claude Compliance API integrations. The platform emphasizes buildtime and runtime protection across the agent lifecycle.

Key Features

Direct Claude Enterprise integration through Anthropic's Compliance API, with posture, activity monitoring, agent governance, and runtime security across Claude, Claude Code, Cowork, MCP servers, plugins, and skills
Full lifecycle coverage spanning buildtime and runtime agent protection
Correlation Agent for contextual, intent-oriented behavior analysis
Microsoft ecosystem integration with M365 Copilot, Copilot Studio, and Microsoft Foundry, plus AWS Bedrock and Google Vertex AI coverage
Adoption among Fortune 500 enterprises, with an enterprise governance focus

Enterprise Governance Focus

Zenity positions itself for organizations requiring comprehensive agent governance at scale. Zenity announced that Gartner discussed the company in its April 17, 2026 research report on the AI agent governance market; Gartner's standard disclaimer, reproduced in Zenity's release, states that inclusion in its research does not constitute an endorsement of any vendor. Deep Microsoft ecosystem integration also makes Zenity relevant for organizations heavily invested in Microsoft Copilot environments.

Agent governance and data-level enforcement solve different halves of the same problem, and the problem crosses surfaces. The same employee runs a local MCP server in Cursor, sends prompts to a remote model, and pulls a file off the endpoint. Nightfall runs one detection brain across all of it, from the AI agent stack to SaaS and the endpoint, adding content-level classification and inline enforcement on the data itself so that governance decisions and data controls stay aligned.

Best For: Enterprises that want a direct Claude Enterprise Compliance API integration combined with broader agent posture, governance, and runtime security across Claude Code, Cowork, MCP servers, plugins, and skills, particularly alongside Microsoft Copilot environments.

3. Straiker Defend AI

Straiker Defend AI provides purpose-built agentic runtime security designed for AI agent workloads rather than adapted from earlier DLP architectures. The platform supports runtime inspection for agent traffic and coverage of emerging agent security frameworks.

Core Capabilities

Purpose-built architecture for agentic workloads, with runtime inspection applied to supported Claude interactions
Runtime detection performance reported by Straiker from its own Claude integration benchmarks
Straiker states that Defend AI maps runtime protections across the ten OWASP Top 10 for Agentic Applications categories, ASI01 through ASI10
Ascend AI offensive testing capability for continuous red teaming
One-line install via API, SDK, or webhook, without thick clients or proxies in those deployment modes

Agent-First Architecture

Straiker supports Claude Code, Cursor, and GitHub Copilot for developer-focused organizations. Its Claude architecture combines an inline runtime path for Claude Code, Claude Chat visibility through the Claude Compliance API, and Cowork monitoring through OpenTelemetry and related mechanisms. Straiker also inventories MCP servers and tools reachable by protected agents for exposure assessment, and deployment via API or SDK supports integration into existing development workflows.

A runtime layer covers agent traffic, and the data itself also keeps moving through SaaS applications, email, browsers, and the endpoint. Nightfall covers the full agentic surface, where AI agents create exfiltration risk, together with those human-driven channels using the same detection engine and the same policies, consolidating DLP, insider risk, and AI governance rather than adding a parallel stack alongside them.

Best For: Organizations that already have comprehensive traditional DLP and want specialized, purpose-built AI agent runtime protection with inline inspection and developer tool coverage.

4. AppOmni

AppOmni delivers SaaS security posture management extending to AI applications. On July 22, 2026, AppOmni announced direct support for Claude Enterprise, ingesting Claude Compliance API data and adding posture and security analysis for Claude organization settings and supported Claude activity.

Platform Scope

SaaS security posture management with AI app coverage
Shadow and unsanctioned SaaS discovery and governance across the enterprise SaaS estate
Direct Claude Enterprise security integration using the Claude Compliance API and Claude configuration data, covering Claude, Claude Code, and Cowork where Anthropic exposes the necessary organization settings or activity APIs
Published list pricing for one of its purchasing dimensions on AWS Marketplace
Integration with existing SaaS security workflows

Anthropic's current documentation notes that Cowork activity via Claude, Claude Desktop, and Claude Mobile is captured in the Compliance API, with OpenTelemetry also available to Team and Enterprise admins for monitoring Cowork activity, and that Cowork and Claude Code session content is in beta. Coverage of those surfaces varies across the market.

Pricing Model

AppOmni is notable for publishing a list price for one of its SaaS security purchasing dimensions on AWS Marketplace. Because AWS supports negotiated private offers for SaaS listings, public list pricing functions as a reference point rather than a predictor of enterprise contract value, and total cost scales with the number of applications covered.

Posture management answers what data exists and how it is configured. Prevention answers whether it leaves. Those are different questions, and prevention does not require posture as a prerequisite, so protection does not have to wait on a months-long catalog of data at rest. Nightfall starts preventing on day one and delivers real discovery and classification as a byproduct, so posture programs can continue in parallel rather than gating protection.

Best For: Organizations prioritizing published list pricing as a reference point and SaaS security posture management that now extends to Claude Enterprise through the Compliance API.

5. Cisco AI Defense

Cisco AI Defense provides AI agent runtime protection that can be centrally managed through Cisco Security Cloud Control alongside supported Cisco security products, making it relevant to organizations already invested in Cisco infrastructure.

Integration Approach

Agent Runtime Protection delivered through an SDK that can monitor and enforce around LLM and MCP calls in instrumented applications
Centralized management for AI Defense and other supported Cisco security products through Cisco Security Cloud Control
Established enterprise support infrastructure
Procurement and management within the broader Cisco security ecosystem, with licensing depending on the products and entitlements purchased

Platform Leverage

Cisco AI Defense benefits organizations with significant Cisco security investments by adding agent runtime protection without introducing new vendor relationships, and Security Cloud Control consolidates management across supported Cisco products.

One scoping note applies to a Claude-specific evaluation. As of August 2026, publicly available documentation describes AI Defense as agent runtime protection around instrumented LLM and MCP applications rather than a documented native Claude Enterprise integration, which does not rule out a private or unpublished integration.

Detection and response platforms and data security platforms are complementary layers that run alongside each other well. Nightfall operates as the data-side control plane across SaaS, endpoint, email, browsers, and every agentic workflow, covering the surfaces where sensitive content actually moves, including the local stdio MCP servers, IDE agents, and desktop apps running on the laptop. Teams comparing architectures can review how cloud, network, and endpoint DLP differ before deciding where each layer belongs.

Best For: Organizations with significant existing Cisco security infrastructure that want agent runtime protection for instrumented LLM and MCP applications through their established vendor relationship.

6. Palo Alto Prisma AIRS

Palo Alto Networks offers multiple Claude security paths rather than a single product. Cortex Cloud Data Security Posture Management (DSPM) integrates with the Claude Compliance API for Claude Enterprise data governance and visibility, while Prisma AIRS provides inline protection for Claude Code using Claude Code hooks and API Intercept.

Enterprise Integration

Cortex Cloud DSPM plus the Claude Compliance API for Claude Enterprise governance and visibility
Prisma AIRS inline protection for Claude Code through Claude Code hooks and API Intercept
AI runtime and agent security across the Prisma AIRS platform
Enterprise-scale deployment capabilities
AWS Bedrock and Microsoft Foundry or Azure AI support, with discovery and runtime enforcement varying by platform and feature

Platform Extension Model

Palo Alto Networks positions AI agent security as an extension of its existing portfolio rather than a standalone purchase, which lets organizations add Claude coverage without introducing a new vendor relationship. Buyers can expect distinct product paths and control planes: the Claude Enterprise governance path runs through Cortex Cloud DSPM, while inline Claude Code protection runs through Prisma AIRS.

Gateway and proxy-based paths route and monitor remote traffic, and Nightfall supports remote MCP as well. The difference is what happens to the content: Nightfall classifies and enforces on the sensitive data flowing through those calls, and it also sits on the laptop where the local stdio server, the Cursor or Claude Code session, and the file an agent just touched all live. Gateway coverage is one capability within a broader AI data security platform. Teams weighing consolidated coverage against multiple control planes can start with the Nightfall comparison hub.

Best For: Organizations heavily invested in Palo Alto Networks infrastructure that want Claude Enterprise data governance through Cortex Cloud DSPM and inline Claude Code protection through Prisma AIRS.

7. Proofpoint

Proofpoint extends its established DLP and insider risk capabilities to Claude Enterprise through the Claude Compliance API. Its May 21, 2026 announcement and its dedicated solution page both describe a Compliance API integration, and Anthropic lists Proofpoint under Claude Compliance API integrations.

Extended Capabilities

Claude Compliance API integration for data security and governance
Extension of supported DLP, data classification, insider risk, and policy workflows to Claude Enterprise activity
Established enterprise customer relationships
Email and collaboration security foundation
Compliance-focused approach for regulated industries

Legacy-to-AI Bridge

Proofpoint offers organizations with existing Proofpoint DLP deployments a path to Claude coverage without wholesale platform replacement. The Compliance API integration extends policy to supported Claude Enterprise activity while maintaining established workflows and compliance configurations.

Established DLP suites were designed around files and email in a pattern-matching era. Nightfall is built the other way around, with content and context aware detection that produces signal instead of noise, applied to the surfaces that matter now, including agents, MCP, browsers, and the endpoint. Teams evaluating both approaches can review the Nightfall vs Proofpoint comparison.

Best For: Organizations with established Proofpoint DLP deployments seeking to extend existing policies and compliance configurations to Claude Enterprise.

How Claude Enterprise Security Integrations Actually Work

Vendor marketing frequently blends several distinct Anthropic mechanisms. Evaluating platforms accurately requires keeping them separate:

Claude Compliance API: The enterprise integration surface that exposes supported Claude conversation, activity, and compliance data to security and governance tools
Inference Hooks: A currently beta Claude Enterprise feature that lets a customer-hosted endpoint inspect supported inference content and return an allow or deny verdict before Claude processes it, across the Claude, Claude Code, and Cowork surfaces Anthropic describes
Claude Code hooks: Claude Code execution and workflow hooks, which are not synonymous with the newer Claude Enterprise Inference Hooks capability
OpenTelemetry for Cowork: A monitoring path available to Team and Enterprise admins for Cowork activity across the organization, alongside Compliance API capture of Cowork activity via Claude, Claude Desktop, and Claude Mobile
stdio MCP: Local process IPC over stdin and stdout, which sits outside network-only inspection of the local tool exchange, and which Nightfall covers directly on the endpoint. For background, see how MCP bypasses traditional tools
Streamable HTTP MCP: The standardized remote network transport defined by the current MCP specification

Why Nightfall AI Stands Out for Claude Enterprise Security

Unified Platform Reduces Tool Sprawl

Nightfall delivers a single solution covering SaaS, endpoints, browsers, email, AI applications, and MCP servers. Instead of managing separate tools for traditional DLP, insider risk, and AI agent security, organizations consolidate onto shared detection and policy intelligence with one vendor relationship. DLP, insider risk, and AI governance used to mean three contracts. Nightfall makes it one platform and one contract, with policy that stays consistent across human and AI activity.

AI-Native Detection Is the Foundation

Nightfall's detection engine uses 100+ ML-based detectors, LLM classifiers across 20+ categories, and custom file classifiers that need no regex, reporting up to 95% precision out of the box and a 99% reduction in false positives, with the up to 95% figure evidenced during a proof of value on customer data. Detectors are customer-trainable and auto-retraining, so precision improves with the environment rather than degrading as data changes. Static rules cannot reason about intent, and a moving actor calls for context-aware detection that decides what is risky first.

MCP Coverage Spans Local and Remote Transports

Many AI agent security tools focus on network traffic, while local stdio MCP runs as process-to-process communication on the device itself. Nightfall provides discovery across local stdio and remote HTTP or SSE MCP transports, per-server risk scoring, tool classification by capability, real-time MCP tool call and tool response enforcement, and prompt injection detection on agent traffic. This matters most for Claude Code and local Cowork or Desktop execution, where stdio-aware controls close a local IPC visibility gap. Remote Cowork sessions do not run local MCP servers, so the relevant control points differ, and Nightfall covers both. Security teams building a rollout plan can follow this MCP monitoring checklist.

Real-Time Control, Not Just Visibility

Seeing the leak is not the win. Stopping it is. Nightfall supports block, coach, redact, delete, revoke, quarantine, and encrypt actions across supported integrations and policy types, with manual and automated approval workflows and full inline blocking on the agentic surface. Claude Code hooks scan and enforce on prompts, MCP tool calls, tool responses, and shell commands; Cowork sessions are covered by an OpenTelemetry audit trail; and Claude Enterprise is monitored through the Compliance API. Continuous telemetry across data-movement channels supplies the forensic story behind every incident, covering who, role, lineage, and prior behavior, with forensic search and app intelligence across all of it.

Autonomous Investigation Reduces SecOps Burden

Nyx, Nightfall's agentic DLP analyst, works as a 24/7 partner in autonomous investigation, surfacing risky users before exfiltration happens, recommending policies and actions, and analyzing patterns across the organization. Nightfall reports that four in five incidents are resolved through automation or employee self-remediation, which shifts security teams from reactive alert triage toward proactive oversight and governance.

Rapid Deployment Accelerates Time to Value

Nightfall reports SaaS integrations in minutes to under one hour and endpoint agents in approximately 30 minutes via MDM, with roughly 10 minutes to connect a first SaaS app or deploy the endpoint agent to hundreds of users and most teams protected the same day. The endpoint footprint of approximately 1% CPU and 50MB RAM keeps user productivity unaffected, and MCP and AI agent coverage rolls out on the same agent already installed.

Claude Integration Depth

Nightfall's Claude Compliance API integration is live and listed by Anthropic as a Claude Compliance API security integration. Nightfall also provides hooks-based enforcement for Claude Code and Compliance API monitoring for Claude Enterprise conversations, files, projects, and activity feed.

AI moves your data. Nightfall controls it. For security teams evaluating AI agent security platforms, Nightfall's combination of unified DLP and AI agent coverage, AI-native detection, MCP transport coverage, and a live Claude Compliance API integration makes it a strong choice for Claude Enterprise environments. Explore Nightfall's AI agent security capabilities or book a demo to see how the platform protects sensitive data as AI adoption accelerates.

Frequently Asked Questions

What makes securing AI agents different from securing human users?

AI agents execute multi-step workflows automatically and access multiple systems at machine speed, while the degree of human review depends on tool permissions, user settings, and organization-level approval policies. Anthropic documents multiple approval modes for Claude Code, and Cowork Enterprise administrators can require fresh approval for permission-gated tool calls. DLP deployments designed around traditional email, endpoint, web, or SaaS channels were built for human-driven data movement, while local stdio MCP traffic is local process communication rather than an HTTP network flow. Organizations need platforms that monitor agent-to-tool communication, detect prompt injection attacks, and enforce policies across both human and AI activity.

Can traditional DLP tools protect sensitive data moved by AI agents?

It depends on the vendor and deployment architecture. Pattern-only detection generates false positives when patterns are broad, and prominent incumbent platforms are no longer limited to regex: Microsoft documents trainable ML classifiers and hybrid classification approaches, and Palo Alto Networks documents ML and LLM enhanced DLP patterns intended to improve accuracy. Several established vendors have also extended coverage to Claude through Anthropic's Compliance API integrations. The practical question is whether a deployment covers the surfaces where agents operate, including endpoints, browsers, MCP tool calls, and IDE workflows, which is exactly the gap AI-native DLP was designed to close.

How does Nightfall AI address the visibility without control problem in AI security?

Many AI security tools provide dashboards showing what AI agents accessed without preventing exposure. Nightfall pairs monitoring with real-time control, supporting block, coach, redact, delete, revoke, quarantine, and encrypt actions along with manual and automated approval workflows. Across Claude surfaces, Claude Code hooks scan and block prompts, MCP tool calls, tool responses, and shell commands, Cowork is covered by an OpenTelemetry audit trail, and Claude Enterprise is monitored through the Compliance API. Continuous telemetry across data-movement channels supplies data detection and response context beyond policy violations alone.

What are the key benefits of a unified platform for AI data security, DLP, and insider risk?

Unified platforms reduce tool sprawl by consolidating overlapping DLP, insider risk, and AI security controls into one stack. A shared detection engine keeps policy consistent across SaaS applications, endpoints, browsers, email, AI tools, and MCP workflows, and security teams manage one console rather than correlating alerts across several. Enterprises increasingly want one vendor rather than three contracts, and consolidated coverage is what closes the gaps between point tools, so teams can stop data exfiltration anywhere it starts.

How important is real-time control in an AI agent security solution?

Inline enforcement matters because AI agents execute and chain data-access actions at machine speed, beyond what manual security response can match. Alert-only controls cannot prevent a transaction that has already completed, although they can support containment, credential revocation, session termination, and prevention of subsequent exposure. Latency is best evaluated inside the target Claude workflow rather than against a universal threshold, because acceptable overhead depends on baseline model latency, how often the control runs, and serial or parallel execution. Note also that not every mechanism enforces: the Compliance API is a monitoring and governance interface rather than an enforcement one, and Anthropic's inline enforcement path, Inference Hooks, is currently in beta for Enterprise plans, which is why inline blocking at the data layer carries the preventive load.

What types of sensitive data can be at risk from unsecured AI agents?

Examples of sensitive information an authorized agent could encounter include customer PII, protected health information, payment card data, source code, API credentials, and proprietary business intelligence. Claude Code can read permitted repository files on demand, including files containing credentials or secrets if access is not restricted, and Anthropic says it sends only the portions needed for the task rather than automatically uploading or indexing an entire repository; administrators and users can also deny access to sensitive paths such as .env. Cowork can access authorized enterprise data through supported built-in connectors and authorized custom or remote MCP connectors, which can allow sensitive information to move across multiple approved systems. Shadow AI introduces additional risk when employees use unauthorized AI tools that lack enterprise security controls. Organizations need platforms that detect and classify sensitive data across every surface where AI agents operate.