Pay As You Go - AI Model Orchestration and Workflows Platform
AI workflows are vulnerable to risks like data breaches, regulatory non-compliance, and compromised model performance. To address these challenges, platforms such as Prompts.ai, Workato, n8n, Tray.ai, and Lakera Guard embed security measures directly into their operations. Key features include role-based access control, audit trails, runtime protections, and compliance with standards like SOC 2, GDPR, and HIPAA.
These platforms ensure secure, scalable AI operations while meeting stringent compliance requirements, making them ideal for industries like finance, healthcare, and government.
| Platform | Certifications | Key Features | Deployment Options |
|---|---|---|---|
| Prompts.ai | SOC 2, GDPR, HIPAA | RBAC, audit trails, runtime protections | Cloud-based |
| Workato | SOC 2, ISO 27001, HIPAA | EKM, AES-256 encryption, Virtual Private Workato | Cloud-based |
| n8n | SOC 2, GDPR | Self-hosting, RBAC, workflow isolation | Cloud or Self-hosted |
| Tray.ai | SOC 2, HIPAA, GDPR | API management, tokenization, data redaction | Cloud-based |
| Lakera Guard | SOC 2, GDPR, NIST | Real-time firewall, threat detection, masking | Cloud or Self-hosted |
These platforms cater to enterprises looking for secure AI solutions with strong compliance and operational safeguards.
Security Features Comparison of 5 AI Workflow Platforms
Prompts.ai takes a proactive approach to security at the enterprise orchestration layer, enabling teams to manage workflows across 35+ top-tier large language models like GPT-5, Claude, LLaMA, and Gemini. Instead of juggling disconnected tools with inconsistent security measures, the platform provides a centralized hub for governance and audits. This unified system eliminates blind spots that can arise when AI workflows operate across multiple environments. Here’s a closer look at how Prompts.ai secures workflows with RBAC, detailed audit trails, and runtime protections.
Prompts.ai implements granular permission settings to control who can access specific models, workflows, and sensitive data. Roles are assigned based on job responsibilities - junior analysts, for example, can be restricted to approved templates, while data scientists may have full configuration access. This structure minimizes the risk of unauthorized access or unintended deployments.
Every action in the system is logged, creating a complete audit trail that details user access, workflow execution times, and data processing activities. The Trend Analyzer tracks performance trends, identifying workflows that deviate from expected behavior or show signs of decline. Built-in deployment monitoring allows teams to quickly identify and address workflow issues. With all activity documented in one centralized system, meeting regulatory compliance requirements becomes far less complex.
The platform’s FinOps layer actively tracks token usage across models, offering real-time monitoring to catch anomalies. Spending thresholds and alerts can be set to flag unusual activity early, ensuring tighter control over resources. By keeping sensitive data within secured environments and applying consistent policies across diverse architectures, Prompts.ai extends protection from static access controls to active runtime oversight.
Workato provides a strong foundation for securing AI workflows, backed by an extensive list of certifications and multi-layered protections. The platform has achieved SOC 1 Type II, SOC 2 Type II, ISO 27001, ISO 27701, and PCI-DSS v4.0.1 Level 1 certifications, while also adhering to standards like HIPAA and IRAP (PROTECTED level). According to GigaOm Research, Workato scored 95.2% for data security and maintained a 99.9% uptime.
Workato goes beyond standard certifications by aligning with NIST 800-171A r2 requirements, making it well-suited for federal agencies and industries with strict regulations. In May 2025, Atlassian and Canva adopted Workato's Enterprise Key Management (EKM), enabling them to use their own encryption keys (BYOK). This feature empowers organizations to manage encryption key lifecycles according to their internal security protocols, a critical capability for protecting sensitive training data and proprietary configurations.
Workato’s RBAC feature enhances security by offering precise permission controls. Permissions are divided into environment roles (managing admin tools and platform settings) and project roles (restricting access to specific workflows, connections, and assets). While admins can see project names, they must join a project to access its details, with all actions logged to ensure accountability. Additional safeguards include password rotation every 90 days and configurable session timeouts ranging from 15 minutes to 14 days, tailored to meet varying security needs.
Workato complements its access controls with robust auditing tools for full visibility. The platform records all user actions in real time, supporting integration with SIEM systems. Workflow execution is tracked through detailed job history logs, while the Agent Insights feature provides transparency into AI agent performance and key metrics. In May 2025, Broadcom leveraged Workato’s custom data retention policies to automate data deletion after specific regulatory periods, reducing breach risks while staying compliant.
To protect data at every stage, Workato uses AES-256 encryption for both data at rest and data in transit. Job histories are additionally encrypted with both global and tenant-specific keys. For added security, Virtual Private Workato (VPW) ensures network isolation for sensitive workflows, and data masking prevents PII from appearing in logs or histories. The platform’s Agent Studio reinforces security by isolating data, verifying user access, and securing authentication, ensuring safe interactions with AI models throughout their execution.
These features combine to create a comprehensive security framework, safeguarding the integrity of AI workflows from start to finish.
n8n offers a privacy-focused solution for secure AI deployments, prioritizing compliance and operational control. With SOC 2 Type 2 and SOC 3 certifications and GDPR compliance, the platform ensures robust data security. Regular independent audits, quarterly vulnerability scans, and annual penetration tests further strengthen its security posture.
n8n meets SOC 2 standards, demonstrating its dedication to safeguarding data. The platform uses strong encryption to protect data both at rest and in transit. For those needing full control over their data, n8n provides a free open-source version that can be self-hosted using Docker or Kubernetes. The cloud version is hosted on Microsoft Azure in Frankfurt’s Germany West Central data center, aligning with European privacy regulations. These measures complement the platform’s detailed access and monitoring capabilities.
RBAC permissions are included in all paid plans, starting at $20/month for 2,500 workflow executions. Workflows and credentials are grouped into isolated "Projects," with project roles determining user permissions for viewing, editing, deploying, or managing. Access to sensitive information is tightly restricted to a "need-to-know" basis. Super admin roles provide oversight for governance, while enterprise plans add SSO, SAML, and LDAP integration for centralized identity management. This layered approach ensures strict access control and comprehensive audit tracking.
n8n features a security audit tool that evaluates credential usage, database vulnerabilities, file interactions, and code-executing nodes. Audit logs are retained for at least 12 months, with the most recent three months readily available for review. Enterprise users can stream workflow events to external tools like Datadog or New Relic via webhooks, enabling real-time monitoring of changes, errors, and usage. The Evaluations feature allows teams to test AI workflows with real datasets, tracking metrics such as accuracy, speed, and semantic similarity before implementing updates.
To safeguard workflows, n8n incorporates a Web Application Firewall (WAF) to block malicious traffic and an Intrusion Detection System (IDS) to identify threats. External secret management is supported through AWS, GCP, Azure, and Vault, eliminating the need for hardcoded credentials. Additional protections include dedicated Guardrails to prevent prompt injection and credential leakage, as well as Fallback Models to ensure continuity by switching to backup AI models if primary services fail. Trace view provides detailed insight into the data flow within each workflow node.
Tray.ai strengthens its security framework with advanced compliance measures and tailored protections for AI workflows, ensuring robust data security and operational reliability.
Tray.ai integrates industry-leading security certifications with specialized workflow protections. The platform holds SOC 1 Type 2 and SOC 2 Type 2 certifications, maintained through rigorous annual audits over a 12-month reporting cycle. It is also HIPAA compliant as a Business Associate and adheres to both GDPR and CCPA standards. Hosting is distributed across three separate AWS regions - US (West), EU (Ireland), and APAC (Sydney) - offering flexible regional data residency options.
Data security is a priority, with encryption protocols in place for both data at rest (AES-256) and in transit (TLS/SSL). Disaster recovery plans aim for a Recovery Time Objective (RTO) of 14 hours and a Recovery Point Objective (RPO) of 24 hours. Tray.ai ensures that customer data is not used for training models, and AI providers are contractually prohibited from retaining prompts or responses after processing. These measures provide critical compliance evidence, data residency options, and reliability reports for legal, IT, and business teams.
The platform implements a four-tier role system: Owner (full control), Admin (full privileges), Contributor (manage and create assets), and Viewer (read-only access). Organizations can segment environments into workspaces by department or project stage (e.g., development, production), ensuring users only access relevant assets. Access is secured with mandatory Single Sign-On (SSO) and robust Two-Factor Authentication (2FA). For AI workflows, the API Management (APIM) framework enforces an "AAA" model - Authenticate, Authorize, and Audit - ensuring only authorized API clients can trigger sensitive processes.
Tray.ai provides comprehensive audit logs that track every action performed on the platform. These logs can be streamed to external systems or SIEM tools for independent oversight and extended retention. Workflow execution logs are stored for 7 to 30 days based on the selected package, though organizations can adjust this to as short as 24 hours or enable "ghost processing", which disables log visibility while maintaining internal redundancy for 24 hours. The APIM framework further tracks API client usage, detailing endpoints accessed, timestamps, and actions taken. Support team access is strictly time-limited and generates specific audit events for compliance purposes.
Tray Guardian enhances AI security with features like tokenization, data redaction, and policy controls to prevent sensitive information from being exposed during processing. The platform integrates over 700 pre-built connectors with built-in security safeguards. Additional tools, such as Crypto and Encryption Helpers (PGP), allow users to manually encrypt or decrypt sensitive data during runtime. All AI interactions, including those through the Merlin Agent Builder and Native AI Connector, are logged to ensure full visibility and accountability.
Lakera Guard serves as a real-time firewall for GenAI applications, actively monitoring and filtering inputs and outputs for issues like prompt injections, jailbreak attempts, PII leaks, and harmful links. In October 2023, the platform achieved SOC 2 Type I certification following an independent audit by Prescient Assurance. It also adheres to EU GDPR regulations and NIST standards. Organizations can deploy Lakera Guard as a cloud-based SaaS or as a self-hosted container for on-premises use, accommodating strict data residency needs.
The platform boasts a 0.01% false-positive rate with latency under 12 milliseconds. Its daily threat intelligence updates, informed by 100,000 adversarial samples, identify over 15,000 threats each day. Lakera's robust security intelligence benefits from its "Gandalf" AI hacking simulator, which has been used by over 1 million players, generating a database of more than 80 million attack data points. With support for over 100 languages, Lakera Guard is well-suited for global operations.
Lakera Guard provides enterprise customers with a three-tier role system accessible through the Lakera Dashboard:
Each user is assigned a unique User ID for auditing purposes. Only Admins have the authority to adjust sensitive settings, such as enabling prompt logging or managing user roles, ensuring that critical security guardrails remain intact.
The platform produces detailed activity logs that track GenAI interactions, user behavior, and firewall events. Specific flags highlight blocked threats and inappropriate content. Logs can integrate seamlessly with security tools like Splunk, Grafana, and ELK. The web-based Security Center offers real-time visibility into application activity, allowing security teams to analyze threats and evaluate policy performance. For self-hosted setups, Lakera Guard supports structured logs to stdout and metrics endpoints for internal observability systems. Additionally, offline batch screening of historical interactions is available for regulatory compliance and reporting needs.
Lakera Guard operates as a centralized policy management system, enabling security teams to oversee defenses across various applications. Its built-in detectors automatically mask or block sensitive data, such as Social Security Numbers, credit card details, and full names, ensuring compliance with GDPR and financial data regulations. The platform is compatible with major model providers like OpenAI, Anthropic, and Cohere, as well as open-source and custom fine-tuned models. Adrian Wood, Security Engineer at Dropbox, shared:
"Dropbox uses AI Agent Security as our security solution to help safeguard our LLM-powered applications, secure and protect user data, and uphold the reliability and trustworthiness of our intelligent features."
These runtime protections lay the groundwork for a deeper security analysis in the forthcoming comparison.
When considering platforms for secure AI workflows, compliance certifications and strong access controls offer critical assurances. Here's a breakdown of key security features that support reliable AI deployments.
Compliance Certifications
Adherence to industry standards is a cornerstone of secure operations. Here's how some platforms measure up:
Publicly available documentation provides less emphasis on certifications for n8n and Lakera Guard. These certifications demonstrate a platform's alignment with regulatory and industry standards.
Access Control Mechanisms
Protecting sensitive data in AI workflows demands robust access controls. Some platforms stand out with specific approaches:
Details on access control measures for Prompts.ai, n8n, and Tray.ai are not extensively covered in available resources. Strong access controls play a crucial role in limiting data exposure, while real-time monitoring adds an extra layer of operational security.
Runtime Protections
Real-time safeguards are essential for maintaining secure workflows during operation:
Information on runtime protections for other platforms is not readily available.
Note: Information on audit capabilities was excluded due to insufficient supporting documentation.
Selecting the right AI workflow platform is about more than just efficiency - it’s about protecting your data while scaling your operations. When sensitive information like proprietary code, customer data, or confidential records flows through AI systems, security and compliance must be priorities, not afterthoughts.
As discussed earlier, the best platforms integrate critical safeguards such as role-based access, encryption, and real-time monitoring to ensure compliance and resilience. Whether you’re in healthcare adhering to HIPAA, financial services tackling SOC 2 Type II audits, or managing GDPR requirements in any sector, you need features like audit trails, strict access controls, and encryption protocols that align with regulatory standards. These elements are non-negotiable for secure AI workflows.
Verify that the platform’s data policies exclude using your proprietary information for model training, and request the latest SOC 2 reports for added assurance. Evaluate whether the architecture separates orchestration from execution - hybrid models allow you to keep data within your infrastructure while benefiting from cloud-based coordination. For industries subject to strict regulations, this separation can be the key to avoiding compliance risks.
Features like zero-retention policies and hybrid execution models show that security and innovation can coexist. Additionally, platforms offering disaster recovery protocols with an 8-hour Recovery Time Objective (RTO) and 24-hour Recovery Point Objective (RPO) ensure your workflows remain operational even during disruptions.
Key security measures for AI workflows focus on safeguarding data and ensuring system integrity. These include encryption for data both in transit and at rest, which protects sensitive information from unauthorized access. Role-based access controls (RBAC) and multi-factor authentication add layers of protection by restricting access to authorized users only. Audit logs play a crucial role in tracking user activity, helping to identify and address potential issues.
Adherence to compliance standards such as SOC 2, GDPR, and HIPAA is critical for maintaining trust and meeting regulatory requirements. Additionally, real-time monitoring and threat detection are vital for identifying vulnerabilities and ensuring the resilience of AI systems throughout their lifecycle.
To make sure a platform won’t use your data for model training, carefully examine its security policies and privacy statements. Pay attention to phrases like "data isolation" or "your data never trains our models." Verify the presence of enterprise-level security measures, relevant compliance certifications, and thorough documentation. It’s crucial to confirm that the platform clearly states your data will not be used for training purposes unless you explicitly give your consent.
The key distinction lies in how data and infrastructure are managed:
