Cloudflare AI Gateway has positioned itself as an edge-based AI traffic management solution, offering routing, caching, observability, and DLP controls for LLM APIs. However, organizations increasingly recognize that traffic routing alone leaves critical gaps in their AI data security posture. While gateways manage how AI traffic flows and can inspect content within routed requests and responses, their controls do not automatically extend to sensitive data moving outside configured gateway paths.
For enterprises seeking cross-surface AI security that addresses real-time data protection, shadow AI prevention, and AI governance, it is essential to evaluate alternatives that go beyond basic gateway functionality. This guide examines seven alternatives that serve different AI infrastructure and security needs in 2026.
Key Takeaways
- AI gateway security remains limited to gateway-routed traffic: AI gateways support traffic management, caching, observability, and DLP enforcement, but those controls apply to configured gateway paths. Direct employee access to AI tools, unproxied copy-paste workflows, and device-level uploads can fall outside that scope
- Cross-surface AI data security covers multiple exfiltration paths: Platforms like Nightfall AI govern sensitive data across supported SaaS applications, endpoints, browsers, email, AI applications, and MCP workflows with consistent detection and policy enforcement
- ML-based detection improves precision: Nightfall reports 95% precision out of the box using transformer-based models, compared with a 5-25% legacy DLP baseline
- Gateway and security layers serve different functions: An AI gateway handles routed traffic, while a dedicated AI data security platform adds DLP, compliance, and threat-prevention controls across supported integrations
- AI agent and MCP security requires specialized coverage: Local stdio MCP servers, IDE-embedded agents, and Claude Cowork activity can operate outside configured gateway paths, making agent-aware and endpoint-aware security controls relevant
1. Nightfall AI
Nightfall AI delivers the unified AI data security platform that provides enterprises real-time visibility and control over data movement by humans and AI agents across SaaS, endpoints, email, browsers, and MCP workflows. Unlike gateway-only solutions that manage traffic routing, Nightfall governs what AI agents and humans do with sensitive data across supported data-movement surfaces.
How Does Nightfall AI Work?
Nightfall's platform uses AI-native detection powered by supervised fine-tuned models to identify sensitive data and enforce policies in real time. Key highlights include:
- Detection Engine: ML detectors for PII, PHI, secrets, credentials, and financial data plus LLM classifiers across 20+ categories achieve 95% detection precision
- Cross-Surface Coverage: Single platform governs data across supported SaaS applications, endpoints, browsers, email, AI applications, and MCP servers
- Real-Time Enforcement: Block, coach, redact, quarantine, delete, encrypt, and revoke permissions based on policies and supported integration capabilities
- AI Agent Security: Covers local stdio and remote HTTP MCP workflows, IDE hooks for Cursor, Claude Code, and VS Code, plus prompt injection detection
Documented Results
Nightfall reports the following benchmarks and customer outcomes:
- Nightfall highlights lower total cost of ownership compared with legacy DLP suites
- An assumed 85% investigation-time reduction used in Nightfall's pricing calculator for AI-based detection and triage
- Unit21 uses Nightfall to "gently redirect our people to the safe gen AI sites" while blocking attempts to put PII or customer data into ChatGPT
Core Capabilities
- SaaS Data Security: Real-time and historical scanning across 12+ apps including Slack, Google Drive, GitHub, Jira, Confluence, Salesforce, and Microsoft 365
- Endpoint Data Security: Single agent covers human and AI/MCP traffic across 10+ vectors with 1% CPU and 50MB RAM footprint
- Shadow AI Prevention: Detects and blocks sensitive data to unsanctioned AI tools while redirecting users to approved alternatives
- Data Discovery and Classification: Historical scanning to find and remediate sensitive data already stored in SaaS applications
- User Coaching: Explains policy violations and allows users to correct or justify actions through self-resolution workflows
What Makes Nightfall AI Unique
- Unified AI data security coverage: Governs data across supported SaaS, endpoint, browser, email, AI application, AI agent, and MCP workflows with consistent policies
- One detection brain across supported surfaces: The same ML and LLM classifiers run across supported SaaS, endpoint, and agentic workflows, including local activity outside a gateway-only inspection path
- MCP security for local and remote workflows: Covers local stdio MCP servers outside unconfigured gateway paths, plus remote HTTP/SSE MCP traffic with tool classification and risk scoring
- Rapid deployment: SaaS integrations and endpoint rollouts supported through API connections and MDM
2. LiteLLM
LiteLLM provides an open-source Python proxy for unified LLM API access, supporting 100+ LLM providers through a single interface.
Primary Focus
LiteLLM focuses on gateway-routed AI traffic with a self-hosted architecture that provides organizations direct control over AI gateway infrastructure. The MIT-licensed solution includes configurable security guardrails for PII masking, blocking, and prompt-injection detection alongside core routing capabilities.
Key Features
- Open-source Python proxy with MIT-licensed core
- Support for 100+ LLM providers including OpenAI, Anthropic, and Google
- Unified API interface across multiple providers
- PII and PHI masking with configurable blocking
- Prompt-injection guardrails and MCP-related security controls
- Load balancing and fallback routing
Coverage Considerations
LiteLLM's self-hosted architecture means organizations maintain infrastructure, security patching, scaling, and maintenance responsibility. The platform provides gateway-level security guardrails for traffic routed through the proxy, while direct employee access to AI tools, copy-paste workflows, and local agent activity not connected to the proxy remain outside that gateway inspection path.
Nightfall Differentiation
Nightfall extends data exfiltration prevention beyond gateway-routed traffic into SaaS applications, endpoint devices, browsers, and email systems. The platform covers local stdio MCP servers and IDE-embedded agents independently of external proxy routing, with consistent ML-based detection across supported integrations.
3. Kong AI Gateway
Kong AI Gateway extends the established Kong Gateway platform with AI-specific capabilities, including PII sanitization and AI traffic routing.
Primary Focus
Kong AI Gateway targets enterprises already standardized on Kong for API management that want to add AI traffic management without introducing new infrastructure. The platform leverages Kong's mature API management capabilities while adding AI-specific routing and security features.
Key Features
- AI features added to existing Kong Gateway infrastructure
- Enterprise API management with AI extensions
- AI PII Sanitizer for detecting and sanitizing sensitive data
- Rate limiting and access controls
- Support for multiple AI providers
- Integration with Kong's broader ecosystem
Coverage Considerations
Kong AI Gateway provides PII sanitization and traffic management for AI requests and responses flowing through the proxy layer. Its gateway inspection applies to configured AI requests and responses, while unrelated SaaS, endpoint, and local agent activity remains outside those routed traffic paths.
Nightfall Differentiation
Nightfall provides unified AI data security across SaaS applications, endpoints, browsers, email, and AI agent workflows with consistent policy enforcement. The platform covers supported data movement beyond gateway-routed traffic, including direct employee access to AI tools, copy-paste workflows, and local stdio MCP servers.
4. Portkey
Portkey delivers a managed AI gateway with LLMOps features, emphasizing prompt management, observability, and multi-provider routing for AI application development teams.
Primary Focus
Portkey focuses on the AI application development lifecycle, providing tools for prompt engineering, experimentation, and production deployment. The platform emphasizes developer productivity and operational visibility into AI application performance with managed infrastructure.
Key Features
- Managed AI gateway with 1,600+ model support
- Prompt management and versioning
- Observability and analytics
- Guardrails for content filtering and PII anonymization
- Multi-provider routing and fallbacks
- Developer-focused integration
Coverage Considerations
Portkey's strength lies in LLMOps capabilities for AI application development with guardrails for content filtering. The platform provides configured gateway-level controls for traffic routed through its infrastructure, with inspection scoped to the AI application and model interactions passing through those paths.
Nightfall Differentiation
Nightfall provides GenAI security across SaaS, endpoints, browsers, email, and AI applications with real-time enforcement. The platform covers data movement beyond gateway-routed traffic, including shadow AI detection, user coaching, and insider threat prevention across supported data-movement surfaces.
5. Lakera Guard
Lakera, now part of Check Point, provides AI agent discovery, risk assessment, and runtime security with prompt injection detection, jailbreak prevention, and data leakage controls for AI applications and agents.
Primary Focus
Lakera specializes in protecting AI applications and agents at runtime with ML-based threat detection. The platform provides security controls at the application layer for organizations building custom AI solutions, with coverage for agent-tool interactions and data leakage prevention.
Key Features
- AI agent discovery and risk assessment
- API-first prompt injection detection
- Jailbreak prevention for LLM applications
- Data leakage prevention controls
- Runtime protection for custom AI apps and agents
- Developer-focused integration
Coverage Considerations
Lakera provides runtime protection for custom AI applications and agents with guardrails for prompt injection, jailbreak attempts, and data leakage. Its agent discovery and risk assessment cover connected agent platforms, while runtime guardrails inspect interactions integrated through the Guard API.
Nightfall Differentiation
Nightfall provides data movement governance across supported SaaS applications, endpoints, browsers, email, and AI workflows with ML-based detection. The platform covers enterprise DLP requirements beyond application-level runtime security, including shadow AI prevention, historical data discovery, and real-time enforcement across supported data-movement surfaces.
6. Prompt Security
Prompt Security, now part of SentinelOne, provides GenAI security through browser, developer, application, and AI agent controls for enterprise AI usage.
Primary Focus
Prompt Security operates within the Singularity Platform, providing AI governance and data leakage controls across employee and agentic AI workflows. The platform targets the challenge of securing employee interactions with AI tools through browser extensions, IDE integrations, and agent monitoring.
Key Features
- Browser extension for AI tool monitoring
- IDE integrations for developer workflows
- Content inspection for AI interactions
- AI agent controls and governance
- Policy enforcement for workforce AI usage
- Integration with SentinelOne ecosystem
Coverage Considerations
Prompt Security provides browser, IDE, and agent-level controls within the SentinelOne ecosystem. Its AI governance and runtime controls cover supported employee and agentic AI interactions through browser, developer, and other configured integrations within the SentinelOne ecosystem.
Nightfall Differentiation
Nightfall provides unified AI data security across SaaS applications, endpoints, browsers, email, AI applications, and MCP servers with consistent ML-based detection. The platform adds direct SaaS scanning across supported collaboration, cloud storage, and communication applications with policy enforcement and user coaching.
7. TrueFoundry AI Gateway
TrueFoundry provides a cloud-agnostic AI deployment platform with gateway capabilities and security guardrails, targeting organizations seeking infrastructure flexibility across multiple cloud providers.
Primary Focus
TrueFoundry emphasizes flexibility across cloud providers with AI deployment and gateway capabilities. The platform allows organizations to deploy AI workloads without lock-in to specific infrastructure vendors, with gateway-level security controls for PII/PHI redaction and prompt-injection detection.
Key Features
- Cloud-agnostic AI deployment
- Gateway capabilities for AI traffic
- Multi-cloud infrastructure support
- PII/PHI redaction at gateway level
- Prompt-injection guardrails
- Model deployment and management
- Cost optimization features
Coverage Considerations
TrueFoundry focuses on AI infrastructure deployment with gateway-level security guardrails for redaction and prompt-injection detection. Its configured gateway controls can inspect model inputs and outputs as well as MCP tool invocations and responses, while SaaS and endpoint activity outside those paths remains a separate security scope.
Nightfall Differentiation
Nightfall extends sensitive data protection beyond AI deployment and gateway traffic into SaaS, endpoint, and agent workflows. The platform provides data movement governance across supported SaaS, endpoint, and agent workflows, with ML-based detection, real-time enforcement, and user coaching independent of infrastructure deployment choices.
Why Nightfall AI Stands Out for AI Data Security
Coverage Beyond Gateway Traffic
AI gateway inspection applies to traffic passing through configured proxy paths. Nightfall governs sensitive data movement across supported SaaS applications, endpoints, browsers, email, AI applications, and MCP workflows. When employees copy-paste sensitive data into ChatGPT, upload files to personal cloud storage, or share credentials through Slack, Nightfall applies real-time policy controls across supported integrations. These data movements fall outside gateway inspection when they are not routed through a configured gateway path.
AI-Native Detection with 95% Precision
Nightfall's detection engine uses transformer-based models trained on labeled sensitive-data examples rather than relying on regular expressions or keyword lists. This approach achieves 95% precision compared to the 5-25% baseline common with legacy DLP and regex-based solutions. High precision means security teams spend time on real risks rather than chasing false positives.
Native AI Agent and MCP Security
Nightfall provides AI agent security that covers supported local and agentic workflows outside configured gateway paths. The platform uses MCP security controls for local stdio MCP servers running on developer machines, IDE-embedded agents in Cursor, Claude Code, and VS Code, plus remote HTTP/SSE MCP traffic. Risk scoring classifies tools by capability: read, read/write, or destructive. Prompt injection detection helps identify adversarial inputs targeting AI systems.
Real-Time Enforcement, Not Just Visibility
Visibility without control is just a dashboard. Nightfall enforces policy in real time with multiple action types: block a paste into ChatGPT, quarantine a Slack message containing customer records, redact PII before it reaches an AI agent, or coach users to correct their behavior. This enforcement capability connects detection to data exfiltration prevention across supported workflows.
Lower Total Cost of Ownership
Nightfall highlights lower TCO compared with legacy DLP suites. Nightfall offers annual per-user pricing through its Foundation Edition and Premier Edition packages. Premier Edition adds supported AI agent, IDE, and MCP security capabilities to the DLP coverage included in Foundation Edition. The pricing calculator's assumed 85% investigation-time reduction illustrates potential operational savings for security teams.
Rapid Time to Value
Nightfall supports rapid deployment through SaaS integrations and MDM-based endpoint rollouts. Connecting a SaaS app can take about 10 minutes, while endpoint rollout time depends on MDM configuration and deployment scope. These workflows support faster initial coverage.
Privacy-First Design for Regulated Industries
Nightfall maintains SOC 2 Type II certification, ISO 27001 certification, HIPAA readiness, and GDPR compliance. The platform supports organizations in financial services, healthcare, and other regulated industries with documented compliance capabilities.
For organizations evaluating alternatives to basic AI gateway solutions, Nightfall's combination of cross-surface coverage, AI-native detection, real-time enforcement, and AI agent security makes it a strong choice for enterprises that need to control how data moves through AI systems. Request a demo to see how Nightfall protects sensitive data across supported AI workflows.
Frequently Asked Questions
What is the difference between an AI gateway and an AI data security platform?
AI gateways like Cloudflare AI Gateway support traffic routing, caching, observability, and DLP controls for LLM APIs. They manage how AI traffic flows and apply their controls to data passing through configured gateway paths. AI data security platforms like Nightfall govern sensitive data across supported SaaS applications, endpoints, browsers, email, and AI agent workflows. Organizations can use a gateway for traffic optimization alongside a data security platform for broader DLP and compliance controls.
Why do organizations need protection beyond AI gateway coverage?
AI gateway controls apply to traffic routed through configured proxy paths. When employees directly access ChatGPT, copy-paste data into unsanctioned AI tools, upload files to personal cloud storage, or share sensitive information through email and collaboration platforms, those activities can fall outside the configured gateway path. Comprehensive AI data security requires coverage across multiple exfiltration paths, not just gateway-routed traffic.
How does Nightfall AI protect AI agents and MCP servers?
Nightfall provides native coverage for local stdio MCP servers, IDE-embedded agents in Cursor, Claude Code, and VS Code, and remote HTTP/SSE MCP traffic. The platform classifies tools by capability (read, read/write, destructive) and applies risk scoring to identify dangerous agent behaviors. Prompt injection detection helps identify adversarial inputs. This coverage addresses local MCP and IDE workflows that operate outside external proxy paths.
What are the key benefits of ML-based detection over regex patterns?
Nightfall reports 95% precision for its ML-based detection, compared with a 5-25% legacy DLP baseline. Regex patterns flag any text matching a defined format, such as treating every 16-digit number as a credit card. ML models weigh context, distinguishing a phone number from a credit card or a test credential from a production secret. This dramatically reduces false positives, allowing security teams to focus on actual risks rather than triaging low-value alerts.
How quickly can organizations deploy Nightfall AI across their environment?
Nightfall supports rapid deployment across SaaS and endpoint environments. Connecting a SaaS application takes approximately 10 minutes through OAuth or API integration. Endpoint agents can be deployed across managed devices via MDM systems, with rollout time depending on the environment. The platform's agent footprint uses 1% CPU and 50MB RAM, supporting lightweight endpoint monitoring and enforcement across supported workflows.
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