ZenGuard integrates seamlessly with your AI infrastructure to deliver real-time security and observability. It analyzes model interactions to detect prompt injections, data exfiltration attempts, adversarial attacks, and suspicious behavior. The platform offers customizable policies, threat intelligence feeds, and audit-ready compliance reports. With a unified dashboard and API-driven alerts, ZenGuard ensures you maintain full visibility and control over your AI deployments across cloud providers.
ZenGuard Core Features
Real-time threat detection
Prompt injection monitoring
Data exfiltration alerts
Adversarial attack detection
Customizable security policies
Unified observability dashboard
Compliance reporting
API-driven alerting
ZenGuard Pro & Cons
The Cons
No mention of open source availability or community contributions
No mobile or extension apps available
Pricing details are summary level, specific costs beyond token limits unclear without contact
Limited publicly available information on integrations or supported AI agent platforms
The Pros
Provides low-latency runtime protection to prevent data leaks and prompt injections within 50ms
Comprehensive adversarial and penetration testing covering 10+ attack categories
Security reports aligned with OWASP and NIST AI frameworks for compliance
Tailored plans for various usage levels including fixed, pay-as-you-go, and custom enterprise solutions
Developed by experienced security engineers from leading tech companies
ZenGuard Pricing
Has free plan
No
Free trial details
Pricing model
Paid
Is credit card required
No
Has lifetime plan
No
Billing frequency
Monthly
Details of Pricing Plan
Fixed Plan
20 USD
Up to 1 million tokens processed per month
Access to all APIs: Prompt Injections, Topicality Control, PII/Secrets/Keywords detection
Continuous improvement of attack detection
Full email and live support (Discord, Slack, etc.)
Pay-As-You-Go
20 USD
Pay $20 per 1M tokens, price decreases to $10 per 1M tokens as usage increases
Configuration support for use-case based AI Agents
SecGPT wraps LLM calls with layered security controls and automated testing. Developers define security profiles in YAML, integrate the library into their Python pipelines, and leverage modules for prompt injection detection, data leakage prevention, adversarial threat simulation, and compliance monitoring. SecGPT generates detailed reports on violations, supports alerting via webhooks, and seamlessly integrates with popular tools like LangChain and LlamaIndex to ensure safe and compliant AI deployments.