Legacy DLP vs. AI interaction security

Firewalls and data loss prevention remain valuable security controls. The question is whether existing controls can inspect the right content and apply the right policies at the point where employees interact with AI.

Keep your existing controls. Evaluate the gaps.

Traditional DLP and network security can inspect and control many data flows. Some products also support cloud applications, APIs, endpoint content, and AI-specific controls. Start with what your current products actually support -not a blanket claim that legacy tools cannot read prompts.

What traditional DLP does well

Classifies data, applies policies, and monitors or blocks transfers across supported endpoints, networks, email, and cloud services.

Where AI creates new questions

Prompts, responses, file uploads, browser interactions, and AI agents may require context-aware inspection in specific workflows.

What to verify

Test the products and integrations: which AI services, data types, channels, actions, and logs are covered?

A practical capability comparison

Conceptual comparison, not a scorecard for every vendor. Actual capabilities depend on product, edition, integrations, policies, and deployment.

Capability	Traditional DLP and security controls	AI interaction security
Network and data-transfer controls	Established capabilities across supported network and transfer paths	Can complement network controls, depending on deployment
Endpoint and file inspection	Available in many products for supported workflows	Can add AI-specific checks where supported
Prompt and response context	Varies by product and integration	Focuses on supported AI interaction workflows
Browser-based AI activity	Depends on browser, endpoint, and application coverage	May provide controls tailored to supported browser-based AI usage
Sensitive-data redaction	Depends on the product and configured actions	Can provide redaction designed for supported AI content
Policy enforcement	Applies policies across supported channels and applications	Can apply controls to supported AI interactions
Audit and investigation	Depends on available logging and reporting	Can provide AI interaction visibility and audit capabilities, subject to configuration

“Not built for prompts” is a question to test - not a universal fact.

Some established DLP vendors are extending platforms with cloud, SaaS, browser, and AI-related controls. Others may have limited visibility into particular workflows. Compare supported integrations and inspection points before concluding that a tool can or cannot inspect prompt content.

Where does inspection happen?

At the endpoint, browser, network, API gateway, or inside an integrated application? What content is visible?

What actions are possible?

Can the control alert, block, redact, quarantine, or route users to an approved tool in the target workflow?

What evidence is retained?

Which events, file interactions, policy decisions, and investigation records can authorized teams review?

Complement your security stack with AI-focused controls.

SecurePledge is an AI interaction security platform, not a blanket replacement for firewalls, endpoint security, or traditional DLP. It can complement existing controls where its supported deployment adds useful inspection, redaction, browser control, or governance capabilities.

Masker

Masker protects AI usage across browsers, AI gateways, desktop applications, and AI agents through a unified security and governance platform. It brings security controls together to help organizations manage AI interactions and apply policies across supported environments.

Redactor

Redactor helps protect personally identifiable information (PII) and protected health information (PHI) by redacting sensitive data in real time. It replaces sensitive information with safe, format-preserving data across supported images, documents, and PDFs before the information leaves your network.

Protector

Protector helps organizations control AI usage within employee browsers and guide users toward secure AI adoption. It supports visibility and security controls for browser-based AI workflows, helping teams manage unauthorized AI tools and reduce the risk of sensitive information being submitted through prompts.

Governor

Governor is the unified governance layer built into every layer of Masker. It supports policy enforcement, auditability, and compliance-related oversight by providing visibility into AI interactions and helping organizations identify and investigate potential policy breaches.

Real-World Applications

Healthcare cybersecurity illustration showing patient records exposed through an AI chat tool

AI Incidents

Patient Data Exposure Through AI Tools: The Johns Hopkins Case

A recent data breach at Czech online supermarket Rohlík highlights a growing challenge for businesses: protecting the sensitive information stored

Cybersecurity shield protecting AI conversations and customer data from unauthorized access

AI Incidents

Rohlík Data Breach: The Hidden Risk of Exposed AI Conversations

A recent data breach at Czech online supermarket Rohlík highlights a growing challenge for businesses: protecting the sensitive information stored

Test the controls against your real AI workflows.

Map the gaps, validate coverage, and choose controls that fit your environment.

DLP and AI security: common questions

Does traditional DLP work with ChatGPT?

It depends on the product, deployment, integration, and exact workflow. Validate supported channels and inspection capabilities.

Are firewalls obsolete for AI security?

No. Firewalls remain important controls and may be combined with tools that inspect relevant AI content and context.

Does SecurePledge replace DLP?

No. It is intended to complement the broader security stack with AI interaction controls in supported workflows.

Can every AI prompt be inspected?

Do not assume universal coverage. Inspection depends on supported tools, integration points, deployment, and configuration.

How should we compare vendors?

Map workflows, data types, control points, policy actions, logs, integrations, and operational needs; validate with testing.