Your Chatbots, Custom LLMs are Powerful. Are They Secure?
Enterprises building internal copilots, RAG assistants, and AI-powered chatbots face critical security risks that traditional tools can't address.
AI-native attacks
Prompt injection and jailbreak attempts manipulate your application to override system instructions, extract hidden data, or bypass security controls.
Sensitive data exposure
Employee PII, customer records, financial data, and API keys leak through prompts and responses.
Regulatory risk
Data leakage and AI-native attacks lead to compliance violations across multiple frameworks. GDPR fines reach €20M. HIPAA penalties hit $2M per violation.
Non-compliant outputs
LLM responses include toxic content, policy violations, or regulated information.
Complete Observability & Security
Input Protection
Analyzes and secures all incoming prompts against AI-related risks.
Output Protection
Monitors model responses for data leaks, compliance breaches, and unsafe content.
Input Protection
Alerting
Policy Enforcement
Reporting
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Input Protection
Alerting
Policy Enforcement
Reporting
Everything You Need to Secure Your AI Deployment
Seamless integration options
Integrate protection into your AI applications without rebuilding your architecture.
SDK Integration Embed directly into application backend (Python, Node.js, Java, Go, C#)
Middleware Proxy Drop-in service between app and LLM
API Gateway Route LLM traffic through secure endpoint
Lambda/Serverless Function-level protection for serverless architectures
Framework Support Compatible with LangChain, LlamaIndex, Haystack, and custom implementations
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Flexible deployment options
Deploy in the environment that meets your security and compliance requirements.
Cloud Deployment Fully managed service in AWS, Azure, or GCP
VPC/Private Cloud Deploy within your virtual private cloud
On-Premise Complete control in your data center
Air-Gapped Environments Isolated deployment without internet access
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AI-Native threat and data leakage detectors built-in
Detect sensitive data and attacks in prompts and responses using AI-native pattern recognition.
PII/PHI detection: SSNs, driver's licenses, passport numbers, addresses, phone numbers, medical records
Financial data: Credit cards, bank accounts, EIN/TIN, routing numbers, financial statements
Secrets & credentials: 100+ sensitive key patterns, including API keys, tokens, passwords, database credentials, cloud keys
Prompt injection & jailbreaks: Instruction overrides, system prompt extraction, delimiter attacks, encoding bypasses
Banned topic detection: Company IP & trade secrets, research & development data, platform-specific data, biometric & genetic data, manipulative content & misinformation
Dual-Layer Protection for Inputs and Outputs
Configure actions based on threat level and application requirements.
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Input Protection:
Redact: Replace sensitive data with placeholders before sending to LLM
Block: Prevent high-risk prompts from reaching the model
Alert: Notify security teams of suspicious prompts
Log only: Monitor inputs without disrupting flow
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Output Protection:
Filter: Remove sensitive data from LLM responses
Sanitize: Clean toxic or non-compliant content
Validate: Ensure responses meet policy requirements
Audit: Track all outputs for compliance
Policy enforcement aligned with compliance frameworks
Pre-built compliance templates
SOC 2, HIPAA, GDPR, PCI DSS, DPDPA
Application-specific rules
Different policies per AI application
Severity levels and risk scoring
Prioritize threats by impact
Custom policy configuration
Define rules for your use case
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What Makes Homegrown App Guard Different
No application disruption
Your AI applications continue operating normally. Protection happens invisibly in <300ms—faster than users can notice.
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Works with any LLM
OpenAI, Claude, Gemini, Azure Bedrock, and local models. Supports any LLM accessible via API.
Privacy-First Architecture
Data processed in memory within your environment—cloud, VPC, or on-premise. Prompts and responses never leave your control. Compatible with air-gapped deployments.
Real World Use cases
Secure customer-facing financial AI assistants
Limit queries to banking services and account information
Block attempts to manipulate the bot into unauthorized transactions
Filter fraudulent or phishing-style queries
Detect social engineering attempts targeting customer data
Ensure responses stay within approved financial topics
Prevent disclosure of internal banking processes or risk models
Block exposure of other customers' information
Validate compliance with financial regulations (PCI DSS, GLBA)
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Protect internal AI portals and employee assistants
Block queries attempting to access confidential repositories
Restrict access based on employee role and clearance level
Detect attempts to extract restricted project information
Filter queries for trade secrets or competitive intelligence
Prevent LLMs from disclosing trade secrets or R&D data
Block exposure of sensitive project details or timelines
Filter proprietary technical documentation
Ensure responses respect information governance policies
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Secure AI-powered customer service chatbots
Filter user inputs for toxic language and abuse
Block spam and repetitive malicious queries
Detect social engineering attempts to extract information
Prevent prompt injection to access backend systems
Prevent chatbot from sharing internal process details
Block disclosure of staff credentials or contact information
Filter confidential company policies or procedures
Ensure responses stay within approved support topics
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