Choosing AI DLP that fits the path

Short answer: standard data loss prevention protects sensitive data across supported endpoints, networks and storage. Promptective focuses on supported workforce AI interactions. It adds AI context such as prompt injection, generated output and proposed agent actions, then applies organisation policy before delivery or execution on supported paths. The controls work together.

A useful Nightfall comparison starts with operating models and control points. Promptective, Microsoft Purview, Netskope One, Prompt Security from SentinelOne, and Palo Alto Networks Enterprise DLP with Prisma AIRS each place controls at different browser, endpoint, network, application or agent boundaries. Build the shortlist around the paths your organisation needs to govern.

This guide compares current public documentation reviewed on 26 August 2026. It summarises documentation; independent product testing remains part of procurement. Feature names, licences and coverage change, so verify the exact application, version, operating system, transport and deployment mode during a controlled pilot.

The difference between standard DLP and AI-specific control

The NIST glossary definition of DLP covers the ability to identify, monitor and protect data in use, in motion and at rest through content inspection and transaction context. The control objective is broad and valuable. Current DLP products can use exact data matching, labels, fingerprints, trainable classifiers, machine learning, image recognition and contextual rules. A fair comparison accounts for that breadth.

AI workflows introduce boundaries that can require additional policy context:

• A user prompt can contain sensitive data or hostile instructions.
• Retrieved documents, webpages and tool results can carry indirect prompt injection.
• A model response can disclose a secret, unsafe code or a prohibited destination.
• An agent can propose a file, shell, Git, cloud or external action.
• A local coding assistant or stdio MCP connection may avoid a browser or network proxy.
• Evidence may need to prove the policy decision and delivery state while limiting retained conversation content.

Products increasingly package DLP and AI security together. Ask whether the same product, an added module or a separate enforcement point supplies each capability.

How Promptective differs from standard DLP

Promptective's public catalogue covers supported browser, code-assistant and custom agent paths. Customer desktop packages are not currently available. Public self-service browser extension delivery is also unavailable; organisations planning a managed browser rollout should confirm the exact browser, provider and entry channels. Organisations normally retain DLP for email, removable media, repositories, SaaS data at rest and other non-AI channels.

Five distinctions matter in an evaluation:

1. The policy unit is an AI interaction. Promptective can evaluate supported prompts, generated output, retrieved context and proposed actions with the destination and interaction stage attached.
2. AI threats sit beside data classification. Supported checks cover secrets, personal and sensitive information, prompt injection, jailbreaks, unsafe generated output and policy-defined agent actions.
3. Enforcement stays near the action. Policy can allow, audit, redact, require approval or block before supported content reaches the provider or before a supported action executes.
4. Routine evidence is content-minimised. Promptective records bounded decision and delivery facts. Its routine remote evidence excludes prompt, response, tool-argument and tool-result plaintext. See the Trust Centre for the data boundary.
5. Coverage is explicit. Browser protection covers supported AI surfaces in managed Google Chrome, Microsoft Edge and Brave deployments. Desktop protection lists ChatGPT and Codex, Claude Code and Cursor, while stating that customer desktop packages are unavailable. Custom agent protection applies when a supported integration submits input, output or an action before delivery or execution.

Promptective is a focused option to evaluate when the requirement centres on workforce AI, local code assistants and agent checkpoints with content-minimised evidence. Selection should remain conditional on customer availability and verification of every deployed path. A broad enterprise DLP or CASB remains useful for sensitive data and cloud activity beyond those AI paths.

Nightfall as the comparison baseline

Nightfall's public documentation describes a broad AI-native DLP platform spanning SaaS, email, endpoints, browser plugins and AI applications. Its endpoint and browser page covers uploads and downloads, clipboard operations, cloud-sync folders, USB, printing and screen capture, plus data lineage, user coaching and self-justification.

For agents, Nightfall's MCP Security page describes discovery across Claude Desktop, Cursor, VS Code and custom integrations. Its gateway inspects and logs MCP requests and responses, applies role and server controls, and scans prompts, file uploads, API calls, tool calls and responses for sensitive data. Published examples include redaction, blocking, quarantine and approval. Nightfall also publishes prompt-injection interception examples for supported agent hooks.

The same MCP page states that every request and response is logged in plaintext for DLP, compliance and forensics. That can support detailed investigations and creates a material data-handling question. Buyers should confirm retention, regional storage, access, redaction, deletion and whether a lower-content evidence mode exists for each deployment.

Nightfall provides a baseline for buyers seeking one vendor across traditional exfiltration channels and newer AI-agent paths. The alternatives below vary in breadth, control point and evidence model.

Alternatives to Nightfall for AI chat and agent workflows

The table is scoped to buyer-relevant capabilities found in primary vendor documentation as of the review date. Each cell includes documented capabilities and procurement points that follow from the stated deployment model.

Microsoft Purview

Purview belongs on the shortlist when Microsoft information protection, Endpoint DLP, audit, eDiscovery and retention are already central to the organisation. Purview DLP in Edge for Business documents allow and block actions for supported text and file uploads, audited outcomes, Intune and Edge prerequisites, and activity in Purview or Defender XDR.

The broader Purview controls for other AI apps span Edge, a Purview browser extension and network data security through SASE or SSE integrations. That page distinguishes sensitive-data DLP from Insider Risk prompt-injection signals and explains when prompt and response content is collected, searchable, retained or available to authorised reviewers. Together, these capabilities provide a strong compliance workflow and make deliberate access and retention design important.

Netskope One

Netskope suits organisations that want AI controls within an existing SSE, CASB and data-security architecture. Its generative AI security page documents shadow-AI visibility, personal-versus-corporate instance context, DLP, user coaching, action controls and AI Guardrails for prompt injection and jailbreaks.

The product family handles these paths separately. Agentic Broker proxies public remote MCP traffic and records session, tool-request and tool-response detail. AI Gateway protects private application-to-model and agent traffic through a virtual appliance, with DLP, guardrails, authentication, rate limits and searchable API logs. A pilot should include local stdio, remote MCP and private API paths separately because their control points differ.

Prompt Security from SentinelOne

Prompt Security documents one platform across workforce AI, code assistants, homegrown applications and autonomous agents. Public capabilities include sensitive-data redaction, prompt-injection and jailbreak protection, real-time blocking and a searchable audit log for agent actions and decisions.

Its Agentic AI Security and Governance page describes an MCP Gateway between agents and servers, with discovery, risk scoring, allow or block policy by user, server or action, and complete searchable interaction logs. It also names a lightweight agent or reverse proxy as enforcement options. Buyers should confirm which data is present in those logs, the default retention and the exact coverage of each code assistant and desktop path.

Palo Alto Networks Enterprise DLP and Prisma AIRS

Palo Alto Networks uses distinct components for workforce and application runtime paths. Enterprise DLP and AI Apps lists NGFW, Prisma Access and Prisma Browser enforcement points, with Enterprise DLP or qualifying CASB and data-security licences.

Prisma AIRS documentation covers AI Runtime Firewall and AI Runtime API for applications, models, data and agents. Runtime checks include prompt injection, sensitive-data leakage, malicious URLs and unsafe output; the API scans prompts and responses and returns actionable recommendations. The documented API use cases include block actions and masking. Procurement should map every required component and licence to the workforce chat, custom application and MCP paths in scope.

A decision framework for the shortlist

1. Inventory the real AI paths

List the actual user, device, application, provider, account instance, data source, model, agent, MCP server, tool and destination. Separate these paths:

• browser chat and embedded AI;
• native desktop and terminal assistants;
• IDE and code-assistant traffic;
• custom application-to-model APIs;
• local stdio MCP;
• remote HTTP or SSE MCP;
• tool results, generated output and external actions; and
• AI-related data at rest in SaaS and repositories.

A feature label such as "ChatGPT protection" needs the vendor to name the consumer or enterprise instance, browser or desktop client, text and file channels, operating systems and enforcement timing.

2. Choose the enforcement point

Decide where policy must run: browser, endpoint, network proxy, cloud API, application SDK, AI gateway, MCP gateway or provider compliance API. Multiple points may be appropriate.

Ask whether the control sees the semantic interaction, reconstructed network content or an after-delivery event. Record whether it can prevent delivery, hold output, stop a tool call or only alert after the action.

3. Define detection requirements

Use the organisation's existing data policy as the starting point. Include exact data matching, labels, fingerprints, custom dictionaries, source code, credentials, PII, financial and health information, images and document classifiers as applicable.

Add AI-specific cases: direct prompt injection, indirect injection in a retrieved document or tool response, prompt extraction, generated secrets, unsafe code, malicious URLs, tool misuse and actions outside authorised scope. Confirm every stage at which each detector runs.

4. Select actions and failure behaviour

Specify which findings should allow, audit, coach, warn with override, redact, require approval, quarantine or block. Bind approvals to the exact user, action, resource, arguments and expiry.

Test stale policy, unavailable cloud analysis, extension failure, endpoint failure, encrypted or pinned traffic, unsupported content and provider drift. Ask what the user sees, whether unsent work is preserved and what evidence records the outcome.

5. Set the evidence and privacy boundary

Set the minimum evidence needed for operations, investigation, compliance and legal hold. Relevant facts can include identity, application, policy and detector versions, finding category, decision, transformation, approval and delivery or execution state.

Then ask which prompts, responses, files, tool arguments, tool results, screenshots and excerpts leave the device or customer boundary. Confirm storage region, encryption, tenant isolation, administrators and support access, subprocessors, model use, retention, legal hold, deletion and export. Detailed plaintext can aid investigation, but it also increases the sensitivity of the evidence store.

6. Price the complete control path

Request a bill of materials tied to the tested architecture. Include endpoint or browser agents, DLP, CASB or SSE, AI guardrails, gateway, agent or MCP modules, logs, SIEM export, retention, support and professional services. Identify seat, device, traffic, token, request and storage meters, and confirm whether a quota can change a security decision.

Procurement questions worth sending every vendor

Coverage and enforcement

• Which exact browser, desktop, IDE, CLI, model, provider, app version, operating system and MCP transport combinations are supported today?
• Which paths are observed, inspected, blocked or held before delivery or execution?
• How are personal and corporate instances of the same AI service distinguished?
• How are local stdio MCP and native desktop traffic governed?
• What happens when confirmed AI traffic is outside the inspection path?
• How quickly does coverage adapt to a provider UI, protocol or client update?

Detection and policy

• Which sensitive-information methods are included: exact match, labels, fingerprinting, classifiers, images, source code and credentials?
• Where do direct and indirect prompt-injection checks run?
• Are model responses, retrieved content, tool arguments and tool results inspected?
• Can policy use user, group, device, app instance, destination, tool, action and data provenance?
• How are customer rules tested, versioned, approved and rolled back?

Actions and user experience

• Which channels support allow, coaching, override, redaction, approval, quarantine and hard block?
• Does a redaction transform only the supported finding while preserving the rest of the work?
• Can output remain held until checks finish?
• Can an approval bind to one exact tool call and argument set?
• What latency and false-positive evidence can the vendor demonstrate on the organisation's pilot corpus?

Evidence, retention and privacy

• Does routine evidence contain full prompts, responses, files, screenshots, tool arguments or tool results?
• Can administrators reconstruct the policy, finding, action and delivery state without conversation plaintext?
• Who can read content, how is access audited and can customer-managed keys be used?
• What are the default and configurable retention periods for events, content and backups?
• How do deletion, legal hold, tenant exit and SIEM export work?
• Which subprocessors, regions and models receive customer content?

Deployment and operations

• Which licences and components are required for every pilot path?
• What MDM, Intune, browser, proxy, certificate, routing, API or application changes are required?
• How are agents, extensions, gateways, rules and trust roots updated and verified?
• What health signal proves the control is active on each device and path?
• How are outages, bypass attempts and unsupported states surfaced?
• Which controls remain available when the service or network is unavailable?

Run a controlled, evidence-led pilot

Use the same approved test corpus and success criteria for every shortlisted product. Keep test data artificial and non-sensitive. Measure each result directly and treat vendor accuracy claims as inputs that require validation. Skip numerical scores unless the organisation has defined measurable criteria and evidence for them.

At minimum, exercise:

1. approved ordinary text on every priority path;
2. a supported sensitive value in typed text and paste;
3. a document upload containing supported sensitive information;
4. sensitive data in a held model response;
5. direct injection in a user prompt;
6. indirect injection in a retrieved document or tool result;
7. a permitted read-only tool call;
8. a prohibited or high-impact tool action;
9. personal and corporate instances of the same provider;
10. stale policy, service outage and disabled enforcement component; and
11. one declared unsupported path.

For every case, record the enforcement point, detector and policy version, action, user experience, latency, provider-delivery or tool-execution state, event content, retained plaintext and cleanup result.

How the controls fit together

A practical architecture typically keeps several controls:

• Web filtering supplies the first destination gate and blocks prohibited services.
• CASB or SSE adds cloud-app discovery, managed-versus-personal instance context and granular activity control.
• DLP carries the enterprise data taxonomy across endpoints, networks, SaaS and repositories.
• AI-specific enforcement adds prompt, retrieval, output and tool-action context at supported AI checkpoints.
• Identity, endpoint management and application controls establish the principal, device posture, permissions and deployment authority.

Promptective fits at the AI-specific layer for supported workforce AI paths and can reuse the organisation's policy intent while producing content-minimised decision evidence. This layered model preserves broad data protection and adds a control that understands supported AI interactions.

Primary sources reviewed

• NIST Computer Security Resource Center: data loss prevention.
• Microsoft: What is a cloud access security broker?.
• Nightfall: AI-powered DLP for endpoints and browsers.
• Nightfall: Model Context Protocol Security.
• Nightfall: AI-native data protection platform.
• Microsoft Learn: Data Loss Prevention for Cloud Apps in Edge for Business, updated 30 June 2026.
• Microsoft Learn: Manage data security and compliance for other AI apps, updated 1 May 2026.
• Netskope: Securing Generative AI with Netskope One.
• Netskope: Agentic Broker.
• Netskope: AI Gateway.
• SentinelOne: Prompt Security.
• Prompt Security: Agentic AI Security and Governance.
• Palo Alto Networks: Enterprise DLP and AI Apps, updated 19 August 2026.
• Palo Alto Networks: Prisma AIRS documentation, updated 21 August 2026.
• Palo Alto Networks developer documentation: Prisma AIRS API use cases.

Sources were checked on 26 August 2026. Vendor documentation describes vendor claims and documented configurations. Validate current availability, contract terms and behaviour in your environment.

Promptective applies organisation policy and records content-minimised evidence on supported browser, desktop and agent AI paths. Compare supported coverage across your priority workflows.