Best AI Agent DLP Solutions for Secure LLM Tool Calls in 2026

AI agents that take action on production systems create data-protection blind spots. When an agent calls a tool, sensitive data moves through prompts, fine-grained tool calls and their arguments, Model Context Protocol (MCP) connections, the API requests and SaaS actions behind each tool, and returned results. Network and endpoint controls see only the paths routed through or observed by their enforcement points, so unmanaged agent-to-tool traffic can still bypass them. AI-agent data protection must account for agent-to-tool and agent-to-agent paths, including identity, destination, and action context, rather than examining prompts alone.

The goal: regain visibility and control over this agentic data path without breaking the workflows the organization is trying to automate.

For tool-using agents, the most useful shortlist starts with where a product enforces policy and where that policy lives. Can it inspect and control prompts, tool arguments, identities, actions, destinations, and returned data on every tool call? Can it enforce the DLP policies your team already maintains, plus custom rules, without rebuilding them in a new console? Mapping these controls to specific agent workloads clarifies which enforcement architecture solves which data-loss risk.

Key takeaways

• For DLP enforcement on every agent tool call: Arcade.dev is an action runtime built for AI agents. It enforces existing and custom DLP policies deterministically where tool calls execute, together with per-user and agent-scoped authorization and credential isolation. It works alongside the other tools on this list, giving users and agents one governed path to their tools.
• For DLP across human and agent surfaces: Nightfall AI and Strac extend data-protection policies across combinations of SaaS, endpoints, browsers, developer agents, and MCP traffic.
• For existing enterprise security stacks: Prisma AIRS, Netskope, and Zscaler are most relevant when an organization already uses those platforms and wants to extend the same security control plane into AI and agent traffic.
• For Microsoft-native data protection: Microsoft Purview fits environments built around Microsoft 365, Entra ID, Copilot, Power Platform, sensitivity labels, and Microsoft compliance workflows.
• For dedicated guardrails and agent posture: Check Point AI Security combines API-based interaction screening with agent discovery and configuration-risk assessment.
• For data-centric security: Cyera combines DSPM, data classification, identity context, and agent posture/runtime controls.
• Shortlist based on the control layer you actually need, then validate the agent platform, identity model, data path, and enforcement behavior in a POC.

Quick comparison: Best AI agent DLP tools at a glance

Primary enforcement point means the architectural location where a product applies controls; observable data path means the traffic or data the product can inspect; control granularity means the level at which a product can define and enforce policy, such as per user, per agent, or per tool call; where policies live means where the policies a product enforces are defined, either in the product's own console or in existing engines it calls; availability/status marks each capability as GA, preview, or early access, with "available" used where a vendor ships a capability without publishing a GA label; and primary use case describes the main architectural or security problem the product addresses. AI security does not automatically mean tool-call DLP. Products differ based on whether they operate at the runtime, prompt boundary, endpoint, SaaS connector, network proxy, Microsoft ecosystem, or data-at-rest layer.

Option	Primary use case	Primary enforcement point	Observable data path	Control granularity	Where policies live	Availability/status
Arcade.dev	Enforcing existing and custom DLP policies on every agent tool call, with per-action authorization and credential isolation in one runtime. Observable data path: Tool arguments, destinations, and results on every tool call the runtime executes	Action runtime	Runtime pre/post tool calls	Per user, per agent, and per tool call, with user permissions intersected with agent-scoped permissions. With the ability to invoke them based on flexible contextual needs.	Existing IdP permissions, plus existing DLP or security engines and custom rules called through Contextual Access hooks	GA, including Contextual Access hooks
Palo Alto Networks Prisma AIRS	Extending an existing Palo Alto security environment across AI gateway, runtime-security, agent-security, and data-protection controls	AI Runtime API/firewall, AI Gateway, and Agent Security	Prompts, model responses, tool calls, MCP/A2A interactions, and agent activity covered by the deployed component	Per user, application, agent identity, agent permission, model, and tool, varying by component	AI security profiles in Strata Cloud Manager, enforced on traffic routed through deployed AIRS components	AI Gateway is GA; Agent Security is part of the current offering; Managed AI Runtime Security remains preview/early access
Nightfall AI	Applying one DLP policy across SaaS, endpoints, developer-agent activity, and MCP workflows	SaaS/API and endpoint DLP plus early-access agent hooks and MCP gateway	SaaS/API payloads, prompts, shell commands, and local, remote, or gateway-routed MCP tool calls where supported	Per user, device, agent, MCP server, tool, and application	Nightfall policies and detectors in the Nightfall console, enforced on connected SaaS, endpoints, and gateway-routed MCP calls	Core DLP GA; AI Agent Security is Early Preview and MCP Gateway is early access
Strac	Applying DLP across managed browsers, endpoints, SaaS applications, and MCP traffic	Endpoint/browser/SaaS controls plus MCP gateway	Browser, endpoint, SaaS, and supported MCP/agent channels	Per user, device, browser, app, agent, tool, and action, varying by enforcement surface	Strac policies in the Strac dashboard, enforced on managed browsers and endpoints, connected SaaS, and gateway-routed MCP calls	Core endpoint/browser/SaaS DLP GA
Microsoft Purview	Microsoft-native data protection and compliance across Microsoft 365, Copilot, Entra-registered agents, and Power Platform	Microsoft ecosystem governance	Microsoft Copilot, M365, Power Platform, and connector paths	Per Entra ID user, sensitivity label, and Microsoft workload	Purview DLP policies and sensitivity labels in the Microsoft Purview portal, enforced on supported Microsoft workloads and Entra-registered agents	Core Purview/DLP GA
Netskope	Extending an existing Netskope SSE/CASB/DLP environment into private AI APIs and MCP traffic	SSE/SWG/CASB, AI Gateway, and Agentic Broker	Proxy and SaaS API paths plus MCP requests, tool calls, and responses routed through Agentic Broker	Per user, device, app, cloud, MCP client, server, tool, and session	Real-time Protection policies and DLP profiles in Netskope One, enforced on traffic routed through Netskope proxies, AI Gateway, and Agentic Broker	Core SSE/CASB GA; Agentic Broker is available and separately licensed
Zscaler	Extending an existing Zscaler Zero Trust environment into MCP/A2A agent communications and AI-related endpoint activity	Zero Trust Exchange/SWG plus AI Broker for MCP and A2A	Proxy-routed AI traffic plus MCP/A2A agent communications routed through AI Broker	Per user, device, application, agent identity, agent permission, and connected data, varying by component	Zero Trust Exchange policies and DLP dictionaries in the Zscaler admin portal, enforced on traffic routed through Zscaler and AI Broker	Core AI controls GA; AI Broker available since June 2026
Check Point AI Security (formerly Lakera)	Dedicated guardrail screening plus cross-platform agent discovery and configuration-risk assessment	Guard API plus AI Agent Security posture and runtime layers	Prompts, model outputs, tool calls, tool responses, and tool descriptions passed through the Guard API	Per conversation, agent, tool, and connected platform	Guard policies assigned per project in the Check Point AI Security dashboard, enforced where the application calls the Guard API	AI Guardrails available; AI Agent Security is early access, with current runtime enforcement through the Guard API
Cyera	DSPM and data classification combined with identity-aware agent posture and runtime controls	DSPM, Omni DLP, Access Trail, and Agent Guardian	Data at rest plus user interactions, tool invocations, and data access or retrieval where Agent Guardian is integrated	Per human or agent identity, data classification, intent, repository, and action	Cyera classifications and policies in the Cyera platform, enforced where Agent Guardian is integrated	DSPM and Agent Guardian available; confirm deployment-specific coverage

Evaluation criteria for AI agent DLP tools

AI agent DLP spans controls that reduce sensitive-data loss across an agent workflow. This includes action runtimes, prompt guardrails, endpoint DLP, SSE proxies, Microsoft-native governance, SaaS DLP, and DSPM.

These architectures are adjacent, not interchangeable. A DSPM tool identifies sensitive data before agents access it. A runtime enforces policy during action execution.

Most products on this list extend an existing security platform into AI traffic. They inspect what passes through their enforcement points but do not execute the tool call, so they see content rather than the action: which tool runs, with which arguments, for which user and agent. That limits both what they catch and how well they stop destructive hallucinations, because a hallucinated bulk delete from an authorized agent carries no sensitive-data pattern to match. The practical architecture keeps these platforms as the source of DLP policy and enforces that policy in an AI-native runtime where tool calls execute.

Selection criterion 1: Enforcement point

The product's architectural position: runtime, prompt/model boundary, API/SaaS connector, endpoint/browser, SSE/CASB proxy, Microsoft ecosystem, or data-at-rest layer.

Selection criterion 2: Observable data path

The product's ability to inspect prompts, tool arguments, destinations, API payloads, files, SaaS records, network traffic, tool results, or data repositories.

Selection criterion 3: Control granularity

How precisely a product can define and enforce policy: per user, per agent, per agent-scoped permission, per tool call, and per destination. Policy that stops at an API's OAuth scopes is coarse, because one write scope can cover every record a user can reach. DLP needs finer control to tell a sanctioned export from a leak.

Selection criterion 4: Enforcement actions

The ability to detect, block, redact, mask, minimize, quarantine, require approval, or authorize or deny execution. Detection and response differ: a product may use probabilistic classification while applying a deterministic block, redaction, approval, or deny action after a policy match.

Selection criterion 5: Auditability and integrations

The availability of SIEM/logging support, immutable audit records, policy reporting, IdP integration, DLP ecosystem fit, MCP/OAuth/API support, and Microsoft or SaaS integration depth.

Selection criterion 6: Deployment, latency, and availability

The deployment model, traffic-routing requirements, runtime integration effort, latency implications for synchronous agents, and GA versus preview status.

Selection criterion 7: Pricing transparency

The public pricing model, listed plan/package details, free tier or trial availability, and billing units shown on first-party pricing or licensing pages.

POC tests for AI agent DLP and tool-call authorization

• External API PII test: Attempt to pass PII as arguments to an external SaaS tool. Verify whether the solution detects, blocks, redacts, or allows the action.
• Destination awareness test: Send the same sensitive payload to an approved internal system and to an external API, Slack channel, or agent handoff. Verify different allow/deny/redact/minimize decisions and logs showing destination classification and reason.
• MCP tool poisoning / indirect prompt injection test: Connect a malicious MCP server whose tool result embeds hidden instructions to call an export tool. Verify the product treats tool output as untrusted, blocks unsafe follow-on calls, and logs the source server, injected-content signal, and blocked action.
• Secrets and token isolation test: Confirm OAuth tokens, refresh tokens, API keys, and injected credentials never enter the prompt, tool result, logs, or LLM-visible context.
• Tool-result minimization test: Return unnecessary sensitive fields from a database lookup. Verify whether the product strips, masks, or blocks them before the model sees them.
• User-agent permission intersection test: Try actions the user can perform but the agent isn't scoped to perform, and actions the agent is scoped for but the user isn't authorized to perform.
• Unsafe multi-step workflow test: Run a realistic sequence such as CRM export to summarization to external email send. Verify scoped tool permissions, destination controls, and human approval for high-risk actions.

Option 1: Arcade.dev

Primary use case

Teams securing multi-user production AI agents that need governed agent tool execution alongside per-action authorization, credential isolation, and pre- and post-tool-call policy enforcement. Arcade.dev handles MCP routing, identity, credentials, tool execution, and policy enforcement in one action runtime, so DLP and MCP governance do not run as separate control planes.

Overview

Arcade.dev is an action runtime for enterprise AI agents that executes tool actions while applying authorization and governance controls at the action layer. It combines existing user identity with scoped agent permissions at runtime, keeps credentials outside the LLM context, and runs policy checks before and after tool execution by allowing, denying, or modifying tool inputs and outputs. Through Contextual Access hooks, those checks can call the DLP and security engines a team already runs or apply custom rules.

Arcade exposes Arcade-hosted tools, custom tools, and remote MCP servers to MCP clients through governed endpoints, so every call from those clients runs through the same authorization and policy checks. These endpoints can use an organization's OIDC identity provider, while remote MCP tools authenticate each end user separately at runtime.

Arcade is model-, framework-, and client-agnostic and deploys in cloud, VPC, on-premises, and air-gapped environments.

Key features

• Delegated multi-user authorization: Enforces the intersection of user identity and agent scope on supported actions.
• DLP checks on every tool call: Pre-execution hooks check tool arguments before data leaves, and post-execution hooks check tool results before they reach the model. The checks apply to every agent action against a tool running in Arcade.
• Sensitive-data detection and remediation: Hooks can call an existing DLP engine or custom logic to detect PII, PCI, PHI, credentials, secrets, prompt-injection content, and risky operations, then allow, deny, redact, mask, or modify the call.
• Existing and custom policies: Enforces the DLP and security policies a team already maintains, plus custom rules, without rebuilding them in a new console.
• Credential isolation and lifecycle management: Keeps OAuth credentials and token handling outside the LLM-visible context.
• Agent action runtime and fine-grained tools: Executes agent-optimized tools scoped to a single action, so policy applies per action instead of per API.
• Audit logging and telemetry: Provides immutable, OpenTelemetry-compatible audit logs, and Arcade.dev sends audit data to the customer's SIEM.

Pricing

Arcade.dev uses a usage-oriented pricing model. Customers can start free, followed by a platform fee plus usage-based metering per user challenge and per tool call, with custom enterprise packaging available.

Key strengths

• Combines action-layer authorization with DLP checks on arguments, destinations, and returned data.
• Keeps OAuth credentials outside the model context while tying execution to user identity and agent scope.
• Closes the bypass path: for tools running in Arcade, agents never hold the credentials, so no call can skip the policy checks.
• Works alongside the other options on this list: existing DLP and guardrail engines stay the source of policy, and Arcade enforces it on every agent tool call.

Where the fit breaks down

• Arcade is designed for the action layer. It doesn't serve as a standalone DSPM tool for discovering legacy sensitive data in cloud storage or SaaS repositories.
• Full per-user delegated authorization depends on routing actions through supported identity/OAuth patterns and supported tool integrations.

Option 2: Palo Alto Networks Prisma AIRS

Primary use case

Organizations already standardized on Palo Alto Networks that want AI data protection, gateway, runtime-security, agent-security, model-security, and security-operations controls integrated into the same security platform.

Overview

Prisma AIRS is Palo Alto Networks' AI Runtime Security offering for securing AI apps, models, agents, and AI-related traffic within the broader Palo Alto security stack. Its controls span runtime APIs, gateway enforcement, agent security, data protection, and traffic governance across supported deployment paths.

Key features

• Network/gateway-layer inspection: Applies controls to AI traffic crossing supported Palo Alto enforcement points.
• Context-aware DLP controls: Protects data using intent, context, and destination awareness across supported enforcement points.
• Prisma Access/SASE integration: Connects AI data monitoring with existing workforce access and traffic-routing policies.
• AI application visibility: Discovers and governs sanctioned or unsanctioned AI application usage across supported environments.
• AI runtime and agent protection: AI Gateway and Agent Security extend controls to supported agent identities, permissions, MCP/A2A interactions, and tool calls. Managed AI Runtime Security includes preview/early-access capabilities.

Pricing

Prisma AIRS uses Palo Alto Networks licensing and Software NGFW credits. AI Runtime API and AI Gateway consumption is measured using token-based usage, while exact costs depend on the deployment and contracted credit pool.

Key strengths

• Useful consolidation path for teams already operating Palo Alto SASE, Prisma, or network security controls.
• Broad visibility across supported AI application, gateway, runtime-security, model-security, and agent-security paths.
• Benefits from existing Palo Alto policy, logging, and security operations workflows where already deployed.

Where the fit breaks down

• Coverage depends on the Prisma AIRS component and integration path deployed. Teams should map which agent, MCP, A2A, API, and in-process execution paths actually pass through AI Gateway, Runtime Security, or Agent Security.
• Prisma AIRS extends a network security platform into AI traffic. It inspects calls that pass through its components but does not execute them, so it evaluates content, not what the call does in the target system. A hallucinated bulk delete from an authorized agent carries no sensitive-data pattern for DLP to match. For per-action OAuth handling, tool-result minimization, and deterministic enforcement inside the execution path, pair it with an AI-native action runtime.
• Migration effort depends on how deeply AI policies, traffic routing, and security operations workflows are tied to Palo Alto infrastructure.

Option 3: Nightfall AI

Primary use case

Security teams that want a common DLP policy across SaaS, endpoints, developer-agent activity, and MCP workflows. Nightfall extends its data detectors and policy model across both human and agent-driven data movement.

Overview

Nightfall AI is a data security platform that applies DLP across supported SaaS, endpoint, browser, API, developer-agent, and MCP surfaces. Its newer agent-security capabilities add discovery and inline controls for supported prompts, shell commands, MCP servers, and tool calls.

Key features

• Sensitive content inspection: Detects sensitive categories such as PII, PCI, PHI, credentials, and secrets across supported inspection paths.
• Automated redaction/remediation: Redacts, masks, or remediates sensitive strings through supported workflows.
• SaaS API integrations: Connects to supported SaaS applications for data inspection and remediation.
• Developer API/SDK: Enables custom application integration for scanning payloads.
• AI-agent and MCP controls: AI Agent Security covers supported developer-agent activity, while the early-access MCP Gateway can enforce policy before gateway-routed tool calls execute, broker credentials, and audit MCP activity.

Pricing

Nightfall prices its platform annually per user across Nightfall Complete and Complete + AI Agent Security packages. Final pricing depends on user count and data volume, and Nightfall offers a seven-day proof of value.

Key strengths

• Applies sensitive-data detection and redaction across supported SaaS, endpoint, API, developer-agent, and MCP workflows.
• Extends the same DLP program across human and agent-driven data movement.
• Centralizes DLP events and remediation workflows for covered integrations.

Where the fit breaks down

• Nightfall can broker credentials and enforce policy for supported MCP Gateway paths, but it is a DLP/security control layer rather than the action runtime that executes arbitrary business-system tools on behalf of users.
• For structured tool-call schemas or domain-specific payloads, validate detector behavior and remediation outcomes during a proof of concept.
• Before committing, evaluate the migration implications for existing DLP integrations and workflows.

Option 4: Strac

Primary use case

Enterprises that want a common DLP model across managed browsers, endpoints, SaaS applications, and MCP traffic, with blocking, redaction, masking, and audit applied across those surfaces.

Overview

Strac is a generative AI DLP platform that inspects prompts, uploads, and supported application channels to block, redact, or mask sensitive data. It provides coverage across supported browser, endpoint, SaaS, and MCP/agent surfaces.

Key features

• Endpoint and browser controls: Inspects supported local and web AI application usage.
• Inline remediation: Warns, blocks, redacts, or masks data in supported channels.
• MCP gateway controls: Inspects gateway-routed MCP tool calls and supports per-tool/per-action allow or block policies, approval gates, and sensitive-data redaction or masking.
• Shadow AI discovery: Identifies unauthorized AI usage where supported by the deployment model.
• Vaulting and masking: Tokenizes, vaults, or masks structured sensitive data across supported workflows.

Pricing

Strac uses quote-based pricing based on the protected surfaces, integrations, data volume, and number of employees in scope.

Key strengths

• Covers human-to-AI leakage paths on managed browsers, endpoints, and supported SaaS channels.
• Applies blocking, warning, redaction, or masking close to the user/device interaction.
• Can complement runtime controls for organizations that also need workforce AI governance.

Where the fit breaks down

• Endpoint/browser controls work best on managed devices and supported channels. Server-to-server autonomous agents may need MCP/API coverage or a separate runtime control.
• If the requirement includes delegated end-user authorization, credential lifecycle management, and execution of business actions in one runtime, evaluate those requirements separately from Strac's MCP gateway access-control model.

Before committing, evaluate the migration implications for the managed-device and policy environment.Option 5: Microsoft Purview

Primary use case

Microsoft-heavy enterprises that want to apply existing Microsoft 365 sensitivity labels, Purview data-security controls, Entra identity context, and Microsoft compliance workflows to supported AI and agent interactions.

Overview

Microsoft Purview is a data security, governance, and compliance suite that includes DLP, sensitivity labeling, data governance, and AI-related governance capabilities. Microsoft extends Purview data-security and compliance capabilities to supported Microsoft and Entra-registered AI agents, with capability coverage varying by the parent AI application.

Key features

• Sensitivity labels: Applies metadata-based protection and access restrictions to supported files, emails, and data.
• Copilot and Microsoft 365 governance: Protects prompts, responses, and interactions across supported Microsoft AI workloads.
• Entra-registered agent coverage: Purview supports specified information-protection and compliance capabilities for Entra-registered agents, while agent authentication and identity governance remain Entra capabilities.
• Insider Risk Management: Correlates risky user behavior with Microsoft ecosystem activity where licensed and supported.
• Power Platform/Dataverse DLP: Applies DLP policies to supported Power Platform and connector usage.

Pricing

Microsoft prices Microsoft 365 E5 at \$60 per user per month, Microsoft 365 E5 without Teams at \$51.45, and the Microsoft Purview Suite add-on at \$12, each paid yearly (Microsoft Purview pricing). Microsoft also offers usage-based Purview capabilities, and actual pricing can vary by agreement.

Key strengths

• Native fit for organizations already standardized on Microsoft 365, Entra ID, Purview, and Copilot.
• Reuses Microsoft sensitivity labels, identity, and compliance workflows where already deployed.
• Simplifies governance for Microsoft-native data, users, Copilot, and Power Platform workflows.

Where the fit breaks down

• Coverage is deepest across Microsoft-native and supported Entra-registered AI scenarios. Purview capabilities vary by application across Copilot Studio, Microsoft Foundry, ChatGPT Enterprise, Claude Enterprise, and other supported AI applications.
• Purview does not execute agent tool calls, and its coverage narrows for agents outside Microsoft's ecosystem, including those built on AWS, Google Cloud, and other clouds. Those agents need an action runtime to enforce DLP policy at the tool call.
• Configuration spans several Microsoft security and compliance products. Buyers must validate operational ownership and policy complexity.
• Migration effort depends on how deeply labels, DLP policies, Entra ID groups, Microsoft 365 data, and compliance workflows are tied to Purview.

Option 6: Netskope

Primary use case

Enterprises already using Netskope SSE, CASB, and DLP that want to extend the same policy environment into workforce AI traffic, private AI APIs, and MCP communications.

Overview

Netskope combines its SSE/CASB data controls with Agentic Broker for MCP communications and AI Gateway for private application-to-model and MCP traffic. These components extend Netskope policy, DLP, access control, and audit capabilities to supported agentic paths.

Key features

• Agentic Broker and MCP controls: Provides visibility and real-time access policies for MCP communications, including the ability to block MCP servers or tool-call events and apply Netskope One DLP where licensed.
• API-based CASB: Discovers sensitive data exposure in supported SaaS platforms.
• Real-time coaching: Warns users during supported risky interactions.
• ZTNA/private app access: Restricts access to private enterprise resources where supported.
• Data classification engine: Identifies sensitive data traversing the proxy using Netskope's data identifiers.

Pricing

Netskope operates on a quote-led enterprise pricing model without public pricing figures.

Key strengths

• Extends existing Netskope SSE, CASB, and DLP controls into supported AI and agentic traffic.
• Applies existing security policy across workforce AI, private AI APIs, and MCP communication paths.
• Connects AI governance with existing SSE, CASB, API gateway, and access-control workflows.

Where the fit breaks down

• Netskope can inspect MCP traffic routed through Agentic Broker and API traffic routed through its AI Gateway. Tool calls that bypass those enforcement paths still require an in-runtime or otherwise inline control. For AI Gateway MCP enforcement in v1.5, Streamable HTTP is the validated transport for full policy enforcement; stdio and legacy SSE-only transports fall outside that validated path.
• Netskope extends an SSE platform into agent traffic. It inspects requests and responses it can route but does not execute tool calls, so it cannot evaluate what a call will do in the target system or stop an undesired action that carries no sensitive data. Pair it with an AI-native action runtime to enforce policy where tool calls execute.
• Migration effort depends on how deeply traffic routing, policy enforcement, and SaaS API integrations are tied to Netskope proxies and connectors.

Option 7: Zscaler

Primary use case

Enterprises already using Zscaler Zero Trust that want to extend the same security control plane into MCP/A2A agent communications, agent access relationships, and AI-related endpoint activity.

Overview

Zscaler combines Zero Trust Exchange controls for workforce web, SaaS, and private application access with AI Broker for supported agent communications. AI Broker extends Zscaler's enforcement model to MCP and A2A interactions, while Agent Registry provides visibility into agent identities and access relationships.

Key features

• AI app categorization: Controls access to supported AI tools and categories.
• Inline DLP inspection: Scans supported outbound payloads for sensitive data.
• AI Broker and Agent Registry: Applies inline access controls to MCP and A2A agent communications and provides a governed view of agent access permissions.
• Exact Data Match: Helps prevent leakage of fingerprinted data records where configured.
• Zero Trust Exchange: Connects users and workloads to authorized applications through Zscaler's Zero Trust architecture.

Pricing

Zscaler uses an enterprise quote-based pricing model without public numerical pricing for AI modules or bundles.

Key strengths

• Extends an existing Zscaler Zero Trust deployment into supported agentic communication paths.
• Applies existing access and DLP controls to supported MCP/A2A agent communications.
• EDM and Zero Trust controls reduce leakage risk across covered traffic paths.

Where the fit breaks down

• Zscaler controls apply to traffic and integrations routed through its enforcement points. Agent actions or in-process execution paths that bypass those controls remain outside that inspection path.
• AI Broker secures agent communications and access but does not execute business-system actions. Zscaler sees traffic, not what a tool call does in the target system, so undesired actions from authorized agents need enforcement in an AI-native action runtime.
• Migration effort depends on network routing, proxy policy design, SSL inspection trust chains, and endpoint/client deployment.

Option 8: Check Point AI Security (formerly Lakera)

Primary use case

Teams that want a dedicated guardrail API for screening agent interactions together with cross-platform agent discovery and configuration-risk assessment.

Overview

Check Point AI Security, formerly Lakera, combines AI Guardrails with an early-access AI Agent Security offering. AI Guardrails screens prompts, model outputs, tool calls, tool responses, and tool descriptions through the Guard API. AI Agent Security adds discovery and configuration-risk assessment across supported agent platforms, while runtime enforcement currently integrates through the Guard API.

Key features

• Prompt defense: Detects direct and indirect prompt attacks in user content, reference material, tool responses, and tool descriptions.
• Data leakage prevention: Detects sensitive data in supported prompts, outputs, tool calls, and tool responses; the API can return locations for application-side masking.
• Agent behavior defense: Applies tool allow/deny lists and off-task-action detection through the Guard API.
• Agent discovery and risk assessment: The early-access product inventories agents, tools, models, authentication, and connected MCP servers on supported platforms.
• API integration: Applications must send each relevant interaction to the Guard API and implement the configured mitigation response.

Pricing

Check Point uses a custom-quote pricing model. Product tiers are available, but numerical list pricing is not.

Key strengths

• Covers prompt attacks and data leakage across supported model and tool-interaction paths.
• Adds early-access posture visibility for agents deployed on supported platforms.
• The Guard API can be integrated at each agent step that requires prompt, output, tool-call, or tool-response screening.

Where the fit breaks down

• Agent discovery and risk assessment are early access, and native platform runtime integrations remain on the roadmap; current runtime enforcement depends on Guard API integration.
• Check Point AI Security does not replace the destination system's identity and authorization controls.
• Before committing, evaluate the migration implications for application-side interaction screening.

Option 9: Cyera

Primary use case

Data security teams that want agent security to inherit context from DSPM, sensitive-data classification, identity mapping, and data-access intelligence.

Overview

Cyera combines DSPM and data classification with DLP, access intelligence, and Agent Guardian. Its DSPM capabilities discover and classify sensitive data, while Agent Guardian maps agents to tools, identities, MCP servers, and data, then applies posture and runtime policies to supported interactions.

Key features

• Agentless discovery and classification: Maps sensitive data across supported cloud, SaaS, hybrid, and on-premises environments.
• Identity and access context: Connects data classification with human and agent identities, access paths, and permissions.
• Agent discovery and posture: Inventories supported agents, tools, models, MCP servers, knowledge bases, and data stores.
• Runtime protection: Evaluates supported user interactions, tool invocations, and data access or retrieval in real time and can alert, quarantine, or block.
• Remediation and audit workflows: Supports guided or automated remediation and records agent interactions with sensitive data.

Pricing

Cyera uses custom-quote pricing. Two comprehensive plans cover DSPM and DLP, with optional add-ons; numerical list rates are not available.

Key strengths

• Connects data-at-rest discovery with identity-aware controls for AI access and agent actions.
• Combines DSPM, DLP, and agent-security controls in one data-centric platform.
• Retains data classification and access context when enforcing supported agent policies.

Where the fit breaks down

• Runtime coverage depends on deploying Agent Guardian through one of its supported integration methods; DSPM alone is not inline tool-call enforcement.
• Buyers should verify support for their agent platform, protocol, and action path during a proof of concept.
• Before committing, evaluate the migration implications for existing classifications, identities, policies, and integrations.

Recommendations by architecture and enforcement requirement

Choose Arcade.dev if…

You're deploying multi-user production agents and need DLP enforced at tool call: existing or custom policies that redact, mask, or block sensitive data in tool inputs and outputs, with per-action authorization, credential isolation, and scoped tool permissions in the same runtime.

Consider Nightfall AI if…

You need a common DLP policy spanning SaaS, endpoints, developer-agent activity, and MCP workflows.

Consider Strac if…

You need a common DLP model across managed browsers, endpoints, SaaS applications, and MCP traffic.

Consider Microsoft Purview if…

Your organization is standardized on Microsoft 365, Entra ID, Copilot, Power Platform, sensitivity labels, and Purview compliance workflows.

Consider Palo Alto Networks Prisma AIRS if…

Your organization is standardized on Palo Alto Networks and wants agent security and AI data protection integrated with its broader gateway, runtime-security, model-security, red-teaming, and security-operations stack.

Consider Netskope if…

You already use Netskope SSE, CASB, or DLP and want to extend the same security policy environment into private AI APIs and MCP-based agent traffic.

Consider Zscaler if…

You already use Zscaler Zero Trust and want to extend that control plane into MCP/A2A agent communications, agent access relationships, and AI-related endpoint activity.

Consider Check Point AI Security if…

You need a dedicated guardrail API together with cross-platform agent discovery and configuration-risk assessment.

Consider Cyera if…

You need DSPM and sensitive-data classification to provide the data context for agent posture, access, and runtime controls.

How to choose the right AI agent DLP tool and run a POC

Step 1: Define your must-haves

• Decide whether you need data discovery/classification, prompt/payload detection, block/redact/minimize enforcement, action authorization, or a combination.
• Require control granularity for production actions: policy that can target the user, agent, tool, destination, destination classification, and scope of each call.
• Separate network-level monitoring from inline tool-call enforcement.

Step 2: Evaluate fit with your existing stack

• Check support for MCP, OAuth, REST APIs, IdP integration, SIEM/logging, existing DLP engines, endpoint management, SSE/CASB routing, and Microsoft Purview where relevant.
• Identify whether deployment requires traffic redirection, endpoint agents, API integration, runtime changes, or Microsoft-native configuration.
• Confirm whether required capabilities are GA, preview, beta, early access, or limited.

Step 3: Test with your use case

• Use the leakage and authorization tests as the pass/fail scorecard.
• Test against a realistic multi-step workflow, such as a CRM query to a summary generation to an external email send, using production-like schemas and permissions.
• Involve the teams that will own policy, runtime integration, incident response, and day-to-day operations.

Step 4: Consider total cost of ownership

• Account for licensing, usage, add-ons, routing changes, endpoint deployment, runtime integration, policy migration, and SIEM/logging work.
• Evaluate latency and failure behavior for synchronous agent workflows.
• Include the future cost of switching away from proprietary policies, routing abstractions, detectors, labels, or runtime integrations.

Conclusion

AI agent DLP becomes a runtime problem when agents move beyond generating text and start taking actions in business systems. At that point, protecting prompts or network traffic alone is not enough. Security controls need visibility into who initiated the action, which agent is acting, what tool is being called, where data is going, and what returns to the model.

Use those requirements to narrow the shortlist rather than selecting on feature count. Keep SASE, CASB, DSPM, endpoint DLP, Microsoft-native controls, and guardrails where they already protect the relevant paths, ,and enforce their policies at the execution layer, where agent tool calls run. For multi-user production agents, Arcade.dev combines governed MCP, per-action authorization, credential isolation, scoped tool access, execution, and pre- and post-tool-call policy enforcement in the runtime.

FAQ

What is the difference between AI DLP and traditional DLP?

Traditional DLP inspects files, endpoints, SaaS apps, and network traffic, and matches content against patterns tied to a user or a channel. AI agent DLP has to evaluate each tool call: which user the agent acts for, which agent is acting, which tool it calls, what arguments it sends, where the data goes, and what the tool returns to the model. An action runtime is the only layer that sees all of that on every call, because it executes the call. When the runtime also holds the credentials, agents have no path to a business system around it, and pre- and post-tool-call policies apply existing or custom DLP rules deterministically before data leaves and before results reach the model.

Why do AI agents need different security than AI chatbots?

Chatbots can read and generate text, while tool-using agents can also take actions in business systems. When agents act, they require least-privilege agent controls, action authorization, scoped tool permissions, credential isolation, and inspection before and after tool execution.

What is the difference between DLP enforcement and action authorization?

DLP enforcement decides whether sensitive data should be blocked, redacted, masked, transformed, or minimized. Action authorization decides whether this user-backed agent is allowed to perform this tool action against this destination at all.

Where should DLP enforcement sit for AI agents?

Enforcement should sit where the sensitive data actually moves. For production agents, that is the action runtime at the MCP or tool-call layer, where policy can evaluate the identity, action, destination, arguments, and returned data before and after execution.

Is an MCP gateway enough to secure AI agent tool calls?

Not on its own. An MCP gateway can centralize routing, access policy, inspection, and audit for traffic that passes through it. For multi-user production agents, also evaluate whether the architecture handles delegated end-user authorization, credential lifecycle, tool execution, non-MCP actions, and governance in the same execution path. Arcade handles MCP routing inside the action runtime, so the layer that routes each call also authorizes it, executes it, and enforces DLP policy on it.

Do I need AI agent DLP if I already have SASE or CASB DLP?

Yes, for agent tool calls. SASE and CASB DLP inspect traffic routed through their proxies, including MCP traffic on platforms that add agentic components. They do not execute tool calls, so they miss calls that never cross the proxy and cannot evaluate what a call will do in the target system. Keep SASE or CASB DLP for the paths it covers, and enforce those policies in an action runtime where agent tool calls execute.

Can prompt guardrails replace AI agent DLP?

No. Prompt guardrails help detect prompt injection, jailbreaks, and unsafe model behavior. They don't replace identity-based action authorization, credential isolation, destination controls, or post-tool-call data minimization.