Lakera Guard Integration Guide | Lakera API documentation

Lakera Guard functions as a control layer around models, assistants, or agents. Whether deployed as a self-hosted container or leveraged via the SaaS service, Lakera Guard fits seamlessly into a wide range of use cases and architectures.

Lakera Guard delivers industry-leading accuracy with minimal latency impact, enabling robust AI security without compromising user experience or application performance.

This guide helps solution architects, security teams, and AI product teams make strategic decisions about integrating Lakera Guard into their systems.

For technical implementation details and recommendations in order to avoid common pitfalls, see the Guard API Documentation.

Key Integration Decisions

1. Choosing Your Screening Flow

Lakera Guard provides the most accurate and comprehensive protection when screening interactions holistically with full context. We recommend screening the complete interaction (all inputs and outputs together) after the LLM response but before showing content to users or sending it downstream.

This approach offers:

Single API call efficiency: One screening request per interaction reduces latency and costs
Complete context: Full conversation and response context enables the most accurate threat detection
Damage prevention: Block threats before any harmful content reaches users or downstream systems
Future-ready architecture: Adding new guardrails requires only policy changes, not redevelopment

Alternative approaches for specific requirements:

Additional input screening: Some customers additionally screen inputs separately before LLM processing to:

Avoid LLM costs on flagged inputs
Prevent sensitive data from reaching third-party LLM providers

Note that this adds latency to the user experience

Parallel input screening: For customers focused primarily on input screening, processing can be done in parallel with LLM inference since Guard typically completes screening before LLM processing finishes. However, this approach limits future expansion to output screening capabilities.

Recommended integration approach: Always include outputs in your screening flow, even if you initially only configure policies for input threats. This ensures you can easily expand protection coverage through policy updates rather than requiring architectural changes.

2. When to Screen Content

Runtime screening is essential for dynamic interactions and should be implemented at every stage where content flows through your system:

User interactions: Screen all user inputs and LLM responses in real-time
Reference content: Any untrusted input can contain prompt attacks so it’s vital to screen all documents, web pages, reference data, or anything else provided to the LLM as context
Agent workflows: Screen each step of multi-turn agent interactions to prevent harmful outputs at any stage
Dynamic content: Screen any content that changes based on user input or external data

Batch/offline screening complements runtime protection for static content:

Knowledge base documents: Screen documents when uploading to identify data poisoning or sensitive information
Template libraries: Validate content templates and standard responses
Historical analysis: Audit past interactions for compliance or threat intelligence

Post-hoc screening and monitoring: Guard supports analysis and monitoring use cases where runtime protection isn’t required:

Compliance reporting: Analyze interactions for regulatory documentation
Threat research: Identify attack patterns and trends in your applications
Performance analysis: Understand content patterns and policy effectiveness for policy fine tuning

Note that Guard offers a specific results endpoint to support analysis.

3. How to Handle Threats

Lakera Guard is deliberately designed to provide flexible threat detection while allowing customers to implement their own response logic. Guard provides a boolean flagging response and optional detailed breakdowns - your application determines the appropriate action.

Common response patterns:

Blocking with appropriate messaging: Prevent flagged content from proceeding, but tailor user communication to the situation:

Don’t reveal attack detection to potential bad actors
Provide helpful guidance when users inadvertently trigger content or data leakage policies
Offer clear paths forward for legitimate use cases

Progressive response: Different threat types may warrant different responses:

High-confidence threats: Immediate blocking
Policy violations: User confirmation or override options, where appropriate. Or logging and monitoring without blocking for low risk situations
Data leakage: Warn the user if sensitive data is found in inputs and prevent egress if found in LLM outputs. You can also choose to mask sensitive data

Monitoring and alerting: Track threats without immediate blocking:

Security team notifications for potential threats
Flagging behavior analysis for policy optimization
Compliance documentation and audit trails

Guard’s flexibility enables you to balance security requirements with user experience considerations specific to your application and user base.

4. Rollout Strategy

Analyze-first approach: Begin with Guard in monitoring mode, or perform historic analysis, to establish baselines, tune policies, and collaborate with Lakera for calibration before enforcement. This approach minimizes user impact while building confidence in detection accuracy.

Calibration: Around 6000 representative data points typically provides sufficient data for detector and policy calibration and false positive reduction.

Graduated enforcement: Implement blocking for high-confidence detections first, then gradually expand coverage as policies are refined.

Full enforcement: For high-security applications, blocking can be implemented immediately with acceptance of an initial calibration period.

Architecture Patterns

Chat Application Pattern

Most suitable for conversational interfaces, customer support bots, and interactive AI assistants.

External inputs, including user input and any reference content, plus the LLM output are screened by Lakera Guard for threats and violations according to the assigned policy. Control flows are designed to prevent any unacceptable output being shown to the user. Optionally, the inputs can be screened prior to processing by the LLM if required.

Recommended approach: Holistic screening after LLM response

Screen the complete interaction (input + output) in a single API call
Implement user-friendly responses for flagged content
Include full conversation history for context-aware detection

Key considerations:

Lakera Guard’s low latency ensures minimal impact on response times
Industry-leading accuracy reduces false positive concerns
Comprehensive threat coverage without architectural complexity
Optional additional screening and response flow of inputs before LLM processing

Document Processing Pattern

Designed for RAG systems, knowledge management, and content analysis workflows.

RAG documents are screened for indirect prompt attacks, poisoning or sensitive data during upload or batch processing to make sure RAG pipeline is secure. Dynamic reference content is screened during runtime alongside user input. Control flows are designed to prevent any unacceptable output being shown to the user. — RAG documents are screened for indirect prompt attacks, poisoning or sensitive data during upload or batch processing to make sure the RAG pipeline is secure. Dynamic reference content is screened during runtime alongside user input. Control flows are designed to prevent any unacceptable output being shown to the user.

Recommended approach: Hybrid screening with offline preprocessing

Screen static documents during upload/ingestion for prompt attack poisoning and sensitive data
Screen dynamic user queries and LLM responses at runtime
Leverage Guard’s large context window and smart internal chunking

Key considerations:

Batch processing enables efficient screening of large document volumes
Runtime screening maintains protection for dynamic interactions
Document versioning supports ongoing content management

AI Gateway Pattern

Centralizes AI security monitoring and enforcement across multiple applications and teams within an organization.

Lakera Guard fits seamlessly into an organization-wide AI Gateway infrastructure. Centrally monitor all LLM traffic for threat observability. Manage policies and flexibly apply runtime security enforcement to applications without any development work by AI application teams.

Recommended approach: Centralized screening with application-specific policies

Implement Guard at the gateway level for consistent security across your organization
Use project-based policies for different applications and environments
Provide observability across all AI interactions across your organization

Key considerations:

Gateway integration enables organization-wide security standards
Use case-specific policies support varying risk requirements
Centralized monitoring provides comprehensive threat visibility
No-code enforcement controls ease the roll out of security across applications

Agent and Workflow Pattern

Supports AI agents using tools, multi-step workflows, and autonomous systems.

Secure tool using agents by screening each agent loop. Screen tool use requests and tool responses to prevent dangerous and unacceptable agent actions before they happen, as well as stop indirect prompt attacks via external systems.

Recommended approach: Contextual screening across agent interactions

Screen each step and loop of agent workflows to prevent prompt attacks and harmful outputs at any stage
Block suspicious tool actions before they take place to prevent damage via downstream or external systems
Include all message types (user, assistant, tool, developer) in screening
Screen both tool inputs and outputs for comprehensive coverage

Key considerations:

The impact of prompt attacks and data leakage can be greater with agents due to their tool use and connections to other internal or external systems
Agent workflows may involve multiple LLM interactions requiring screening at each step
Tool use responses may introduce indirect prompt attacks and unexpected or malicious content requiring validation

Next Steps

Implementation: Review the Guard API Documentation for specific implementation details and advice to avoid common pitfalls
Quick Start: Try the Developer Quickstart for hands-on experience
Policy Configuration: Learn about Policies and Projects for customization
Calibration Planning: Contact support@lakera.ai for architectural guidance and collaborative policy and detector optimization