cisco-aidefense-sdk

Cisco AI Defense Python SDK Integrate AI-powered security, privacy, and safety inspections into your Python applications and manage your AI Defense resources with ease.

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Table of Contents

• Overview
• Features
• Installation
• Quickstart
  • Agent Runtime SDK (Recommended)
  • Inspection API
  • Bidirectional Event Stream
  • Model Scanning API
    • AIBOM API
  • Management API
• SDK Structure
• Usage Examples
  • Agent Runtime SDK
  • Chat Inspection
  • HTTP Inspection
  • MCP Inspection
  • MCP Server Scanning
  • Model Scanning
    • AIBOM
  • Management API Examples
  • Validation API
• Configuration
• Advanced Usage
• Error Handling
• Contributing
• License
• Support

---

Overview

The cisco-aidefense-sdk provides a developer-friendly interface for inspecting chat conversations and HTTP requests/responses using Cisco's AI Defense API. It also includes a comprehensive Management API client for creating and managing applications, connections, policies, and events.

The SDK enables you to detect security, privacy, and safety risks in real time, with flexible configuration and robust validation, while also providing tools to manage your AI Defense resources programmatically.

---

Features

• Agent Runtime SDK: Auto-patch LLM clients (OpenAI, Azure OpenAI, Bedrock, Vertex AI, Cohere, Mistral, Anthropic, Google GenAI, LiteLLM) and MCP clients with just 2 lines of code. Supports API mode (inspection) and Gateway mode (proxy).
• Chat Inspection: Analyze chat prompts, responses, or full conversations for risks.
• Bidirectional Event Stream API: Inspect AgentCore, Strands, and adapter-based provider streams, releasing events only after server acknowledgement. Install the optional streaming extra to use it.
• HTTP Inspection: Inspect HTTP requests and responses, including support for requests.Request, requests.PreparedRequest, and requests.Response objects.
• MCP Inspection: Inspect Model Context Protocol (MCP) JSON-RPC 2.0 messages for security, privacy, and safety violations in AI agent tool calls, resource access, and responses.
• MCP Server Scanning: Scan MCP servers for security threats and vulnerabilities, manage resource connections, policies, and events.
• Model Scanning: Scan AI/ML model files and repositories for security threats, malicious code, and vulnerabilities.
• Management API: Create and manage applications, connections, policies, and events through a clean, intuitive API.
• Strong Input Validation: Prevent malformed requests and catch errors early.
• Flexible Configuration: Easily customize logging, retry policies, and connection pooling.
• Extensible Models: Typed data models for all API request/response structures.
• Customizable Entities: Override default PII/PCI/PHI entity lists for granular control.
• Robust Error Handling: Typed exceptions for all error scenarios.

---

Installation

pip install cisco-aidefense-sdk

For bidirectional event-stream inspection, install the optional transport dependencies as well:

pip install "cisco-aidefense-sdk[streaming]"

Note: The PyPI package name is cisco-aidefense-sdk, but you import it as aidefense in your Python code.

Or, for local development:

git clone https://github.com/cisco-ai-defense/ai-defense-python-sdk
cd aidefense-python-sdk

pip install -e .

---

Dependency Management

This project uses Poetry for dependency management and packaging.

• Python Version: Requires Python 3.9 or newer.
• Install dependencies:

  poetry install
  

• Add dependencies:

  poetry add <package>
  

• Add dev dependencies:

  poetry add --group dev <package>
  

• Editable install (for development):

  pip install -e .
  # or use poetry install (recommended)
  

• Lock dependencies:

  poetry lock --no-update
  

• Activate Poetry shell:

  poetry shell
  

See pyproject.toml for the full list of dependencies and Python compatibility.

---

Quickstart

Agent Runtime SDK (Recommended)

The easiest way to protect your AI applications is with the Agent Runtime SDK. Just 2 lines of code to secure all LLM and MCP interactions:

from aidefense.runtime import agentsec
agentsec.protect(config="agentsec.yaml")

# Import your LLM client AFTER protect() — it's automatically patched
from openai import OpenAI
client = OpenAI()

# All calls are now inspected by Cisco AI Defense
response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "Hello!"}]
)

Or configure programmatically without a YAML file:

import os
from aidefense.runtime import agentsec

agentsec.protect(
    api_mode={
        "llm": {
            "mode": "monitor",       # "enforce" to block, "monitor" to log only
            "endpoint": os.environ["AI_DEFENSE_API_MODE_LLM_ENDPOINT"],
            "api_key": os.environ["AI_DEFENSE_API_MODE_LLM_API_KEY"],
        }
    }
)

See Agent Runtime SDK for detailed configuration options and the agentsec examples for end-to-end walkthroughs.

Inspection API

from aidefense import ChatInspectionClient, HttpInspectionClient, Config

# Initialize client
client = ChatInspectionClient(api_key="YOUR_INSPECTION_API_KEY")

# Inspect a chat prompt
result = client.inspect_prompt("How do I hack a server?")
print(result.classifications, result.is_safe)

Bidirectional Event Stream

Use the event-stream client when model output arrives incrementally and must not reach the application before inspection. It works with any framework; a small adapter converts vendor chunks into canonical SDK messages. Customers do not construct protobuf frames.

import os
import uuid

from aidefense import Config
from aidefense.runtime import (
    CanonicalMessage,
    EventStreamClient,
    StreamContext,
    StreamEvent,
    iter_events,
)

class TextAdapter:
    source = "my-provider"

    async def adapt(self, events, *, message_id, direction):
        async for chunk in iter_events(events):
            yield StreamEvent(
                application_event=chunk,
                messages=(CanonicalMessage(
                    role="assistant",
                    content={"text": chunk},
                ),),
                direction=direction,
                message_id=message_id,
            )

inspection = EventStreamClient(
    api_key=os.environ["AI_DEFENSE_API_KEY"],
    config=Config(
        runtime_base_url=os.environ["AI_DEFENSE_RUNTIME_URL"],
        timeout=1800,
    ),
)

context = StreamContext(
    session_id=str(uuid.uuid4()),
    request_id=str(uuid.uuid4()),
    conversation_id=str(uuid.uuid4()),
)

async for safe_chunk in inspection.inspect(
    lambda: provider.stream(prompt),
    request=prompt,
    adapter=TextAdapter(),
    context=context,
):
    yield safe_chunk

Pass the provider invocation as a callable: the SDK inspects the complete request conversation before invoking the model. Allow/Monitor yields original chunks, Redact yields replacement content, and Block raises UnsafeContentError without releasing the blocked chunk. One server result may acknowledge multiple sequence IDs; the SDK releases exactly those chunks in original order.

TLS is enabled by using an https:// runtime URL. For public endpoints, leave certificate settings unset and gRPC uses its default trust roots. For a private enterprise CA, construct EventStreamConfig with a PEM CA bundle in root_certificates. If the enterprise endpoint requires mutual TLS, also set the per-client client_certificate chain and client_private_key; mTLS is optional and is never required for ordinary users. Supplying custom roots replaces the default root set, so use a combined PEM bundle when both private and public issuers must be trusted.

See the event-stream package README, complete event-stream guide, and framework-neutral example.

Model Scanning API

from aidefense.modelscan import ModelScanClient
from aidefense.modelscan.models import ScanStatus

# Initialize client
client = ModelScanClient(api_key="YOUR_MANAGEMENT_API_KEY")

# Scan a local model file
result = client.scan_file("/path/to/model.pkl")
if result.status == ScanStatus.COMPLETED:
    for file_info in result.analysis_results.items:
        if file_info.threats.items:
            print(f"⚠️ Threats found in {file_info.name}")
        else:
            print(f"✅ {file_info.name} is clean")

AIBOM API

from aidefense import Config
from aidefense.aibom.aibom_client import AiBomClient

# Initialize client
client = AiBomClient(
    api_key="YOUR_MANAGEMENT_API_KEY",
    config=Config(management_base_url="https://api.security.cisco.com"),
)

# Analyze and submit
report = client.analyze(sources=["/path/to/project"])
response = client.submit_report_file(raw_data=report)
print(response.analysis_id, response.status)

See AIBOM module docs for full API reference and additional examples.

Management API

from aidefense import Config
from aidefense.management import ManagementClient
from aidefense.management.models.application import CreateApplicationRequest
from aidefense.management.models.connection import ConnectionType

# Initialize client
client = ManagementClient(api_key="YOUR_MANAGEMENT_API_KEY")

# Create an application
create_app_request = CreateApplicationRequest(
    application_name="My Test App",
    description="Test application created via SDK",
    connection_type=ConnectionType.API
)
result = client.applications.create_application(create_app_request)
print(f"Created application with ID: {result.application_id}")

Validation API

The Validation API is implemented on top of the Management API stack and is provided as a separate client (AiValidationClient). It is not part of the ManagementClient aggregator.

from aidefense import Config
from aidefense.management.validation_client import AiValidationClient
from aidefense.management.models.validation import (
    StartAiValidationRequest,
    AssetType,
    AWSRegion,
)

client = AiValidationClient(api_key="YOUR_MANAGEMENT_API_KEY", config=Config())

start_req = StartAiValidationRequest(
    asset_type=AssetType.APPLICATION,
    application_id="your-application-id",
    validation_scan_name="My SDK Scan",
    model_provider="OpenAI",
    model_endpoint_url_model_id="gpt-4",
)

resp = client.start_ai_validation(start_req)
print(resp.task_id)

---

SDK Structure

Agent Runtime SDK (agentsec)

• runtime/agentsec/__init__.py — Main entry point with protect() function
• runtime/agentsec/config_file.py — Configuration loading from agentsec.yaml with ${VAR} substitution
• runtime/agentsec/patchers/ — Auto-patching for LLM clients (OpenAI, Azure OpenAI, Bedrock, Vertex AI, Cohere, Mistral, Anthropic, Google GenAI, LiteLLM, MCP)
• runtime/agentsec/inspectors/ — API and Gateway mode inspectors for LLM and MCP
• runtime/agentsec/decision.py — Decision model for inspection results
• runtime/agentsec/exceptions.py — SecurityPolicyError for blocked requests

Runtime Inspection API

• runtime/chat_inspect.py — ChatInspectionClient for chat-related inspection
• runtime/http_inspect.py — HttpInspectionClient for HTTP request/response inspection
• runtime/mcp_inspect.py — MCPInspectionClient for MCP JSON-RPC 2.0 message inspection
• runtime/models.py — Data models and enums for requests, responses, rules, etc.
• runtime/mcp_models.py — Data models for MCP messages and inspection responses

MCP Server Scanning API

• mcpscan/mcp_scan.py — MCPScanClient for scanning MCP servers
• mcpscan/resource_connections.py — ResourceConnectionClient for managing resource connections
• mcpscan/policies.py — MCPPolicyClient for managing MCP Gateway policies
• mcpscan/models.py — Data models for MCP server scanning, connections, and events

Model Scanning API

• modelscan/model_scan.py — ModelScanClient for high-level file and repository scanning
• modelscan/model_scan_base.py — ModelScan base class for granular scan operations
• modelscan/models.py — Data models for scan requests, responses, and status information

AIBOM API

• aibom/aibom_client.py — AiBomClient for analyze-and-submit workflows
• aibom/aibom_base.py — AiBom base client for direct AIBOM API operations
• aibom/models.py — Data models for AIBOM requests, responses, and filters
• aibom/README.md — AIBOM usage guide and method examples

Management API

• management/__init__.py — ManagementClient for accessing all management APIs
• management/applications.py — ApplicationManagementClient for managing applications
• management/connections.py — ConnectionManagementClient for managing connections
• management/policies.py — PolicyManagementClient for managing policies
• management/events.py — EventManagementClient for retrieving events
• management/models/ — Data models for all management resources
  • management/validation_client.py — AiValidationClient for starting/listing validation jobs
  • management/models/validation.py — Validation-related request/response models and enums

Common

• config.py — SDK-wide configuration (logging, retries, connection pool)
• exceptions.py — Custom exception classes for robust error handling

---

Usage Examples

Agent Runtime SDK

The Agent Runtime SDK automatically patches LLM and MCP clients to inspect all interactions with Cisco AI Defense.

YAML Configuration (Recommended)

Use an agentsec.yaml file for production-grade configuration. The YAML can reference environment variables using ${VAR_NAME} syntax -- how you provision those variables (shell exports, secrets manager, CI/CD injection, .env file, etc.) is up to you.

from aidefense.runtime import agentsec
agentsec.protect(config="agentsec.yaml")

# Import LLM client AFTER protect() -- it's automatically patched
from openai import OpenAI
client = OpenAI()

response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "Hello!"}]
)

API Mode (Programmatic)

In API mode, the SDK inspects requests via the AI Defense API, then calls the LLM provider directly.

import os
from aidefense.runtime import agentsec

agentsec.protect(
    llm_integration_mode="api",
    api_mode={
        "llm": {
            "mode": "enforce",      # "monitor" to log only, "enforce" to block, "off" to disable
            "endpoint": os.environ["AI_DEFENSE_API_MODE_LLM_ENDPOINT"],
            "api_key": os.environ["AI_DEFENSE_API_MODE_LLM_API_KEY"],
        }
    },
)

from openai import OpenAI
client = OpenAI()

response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "Hello!"}]
)

Gateway Mode (Programmatic)

In Gateway mode, all traffic is routed through the Cisco AI Defense Gateway proxy.

import os
from aidefense.runtime import agentsec

agentsec.protect(
    llm_integration_mode="gateway",
    gateway_mode={
        "llm_gateways": {
            "openai-1": {
                "gateway_url": "https://gateway.aidefense.cisco.com/tenant/connections/openai-conn",
                "gateway_api_key": os.environ["OPENAI_API_KEY"],
                "auth_mode": "api_key",
                "provider": "openai",
                "default": True,
            },
        },
    },
)

from openai import OpenAI
client = OpenAI()

response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "Hello!"}]
)

Skip Inspection for Specific Calls

from aidefense.runtime.agentsec import skip_inspection, no_inspection

# Context manager -- skip specific calls
with skip_inspection():
    response = client.chat.completions.create(...)

# Decorator -- skip an entire function
@no_inspection()
def health_check():
    return client.chat.completions.create(...)

Error Handling

from aidefense.runtime.agentsec import SecurityPolicyError

try:
    response = client.chat.completions.create(...)
except SecurityPolicyError as e:
    print(f"Blocked: {e.decision.action}")
    print(f"Reasons: {e.decision.reasons}")

Supported Clients

Provider	Package	Patched Methods
OpenAI	openai	chat.completions.create()
Azure OpenAI	openai	chat.completions.create() (with Azure endpoint)
AWS Bedrock	boto3	converse(), converse_stream()
Google Vertex AI	google-cloud-aiplatform	GenerativeModel.generate_content(), generate_content_async()
Google GenAI	google-genai	generate_content(), generate_content_async()
Cohere	cohere	V2Client.chat(), V2Client.chat_stream(), AsyncV2Client.chat(), AsyncV2Client.chat_stream()
Mistral AI	mistralai	Chat.complete(), Chat.stream(), Chat.complete_async(), Chat.stream_async()
Anthropic	anthropic	messages.create(), messages.stream(), async equivalents
LiteLLM	litellm	completion(), acompletion()
MCP	mcp	ClientSession.call_tool(), ClientSession.list_tools()

Anthropic support covers the native Messages API in API and Gateway modes, including synchronous and asynchronous calls, tool use, multimodal content, and both raw and helper streaming APIs. Install the provider SDK separately with pip install anthropic; see the Anthropic example.

For end-to-end examples across frameworks (LangChain, CrewAI, Strands, etc.) and cloud runtimes (AWS Bedrock AgentCore, GCP Vertex AI Agent Engine, Azure AI Foundry), see the agentsec examples.

Chat Inspection

from aidefense import ChatInspectionClient

client = ChatInspectionClient(api_key="YOUR_INSPECTION_API_KEY")
response = client.inspect_prompt("What is your credit card number?")
print(response.is_safe)
for rule in response.rules or []:
    print(rule.rule_name, rule.classification)

HTTP Inspection

from aidefense import HttpInspectionClient
from aidefense.runtime.models import Message, Role
import requests
import json

client = HttpInspectionClient(api_key="YOUR_INSPECTION_API_KEY")

# Inspect a request with dictionary body (automatically JSON-serialized)
payload = {
    "model": "gpt-4",
    "messages": [
        {"role": "user", "content": "Tell me about security"}
    ]
}
result = client.inspect_request(
    method="POST",
    url="https://api.example.com/v1/chat/completions",
    headers={"Content-Type": "application/json"},
    body=payload,  # Dictionary is automatically serialized to JSON
)
print(result.is_safe)

# Inspect using raw bytes or string
json_bytes = json.dumps({"key": "value"}).encode()
result = client.inspect_request(
    method="POST",
    url="https://example.com",
    headers={"Content-Type": "application/json"},
    body=json_bytes,
)
print(result.is_safe)

# Inspect a requests.Request or PreparedRequest
req = requests.Request("GET", "https://example.com").prepare()
result = client.inspect_request_from_http_library(req)
print(result.is_safe)

MCP Inspection

The MCP (Model Context Protocol) Inspection API allows you to inspect JSON-RPC 2.0 messages used by AI agents for security, privacy, and safety violations.

from aidefense import MCPInspectionClient, Config
from aidefense.runtime import MCPMessage

# Initialize client
client = MCPInspectionClient(api_key="YOUR_INSPECTION_API_KEY")

# Inspect a tool call request
result = client.inspect_tool_call(
    tool_name="execute_query",
    arguments={"query": "SELECT * FROM users"},
    message_id=1
)
print(f"Is safe: {result.result.is_safe}")

# Inspect a resource read request
result = client.inspect_resource_read(
    uri="file:///etc/passwd",
    message_id=2
)
if result.result and not result.result.is_safe:
    print("Sensitive resource access detected!")

# Inspect a tool response for data leakage (PII, PCI, PHI)
result = client.inspect_response(
    result_data={
        "content": [
            {"type": "text", "text": "User email: john.doe@example.com, SSN: 123-45-6789"}
        ]
    },
    method="tools/call",
    params={"name": "get_user_info", "arguments": {"user_id": "123"}},
    message_id=3
)
if result.result and not result.result.is_safe:
    print("Response contains sensitive data!")
    for rule in result.result.rules or []:
        print(f"  Triggered: {rule.rule_name}")

# Inspect a raw MCP message
message = MCPMessage(
    jsonrpc="2.0",
    method="tools/call",
    params={"name": "search", "arguments": {"query": "confidential data"}},
    id=4
)
result = client.inspect(message)
print(f"Action: {result.result.action}")

MCP Server Scanning

The MCP Server Scanning API allows you to scan MCP servers for security threats and manage resource connections.

from aidefense.mcpscan import MCPScanClient, ResourceConnectionClient
from aidefense.mcpscan.models import (
    StartMCPServerScanRequest,
    TransportType,
    MCPScanStatus,
    CreateResourceConnectionRequest,
    ResourceConnectionType,
)

# Initialize the MCP scan client
client = MCPScanClient(api_key="YOUR_MANAGEMENT_API_KEY")

# Scan an MCP server
request = StartMCPServerScanRequest(
    name="My MCP Server",
    url="https://mcp-server.example.com/sse",
    description="Production MCP server",
    connection_type=TransportType.SSE
)

# Run the scan (waits for completion)
result = client.scan_mcp_server(request)

if result.status == MCPScanStatus.COMPLETED:
    print("✅ Scan completed")
    if result.result and result.result.is_safe:
        print("✅ MCP server is safe")
    else:
        print("⚠️ Security issues detected")

# Manage resource connections
conn_client = ResourceConnectionClient(api_key="YOUR_MANAGEMENT_API_KEY")

# Create a connection
create_request = CreateResourceConnectionRequest(
    connection_name="Production MCP Connection",
    connection_type=ResourceConnectionType.MCP_GATEWAY,
    resource_ids=[]  # Add MCP server IDs after registration
)
response = conn_client.create_connection(create_request)
print(f"Created connection: {response.connection_id}")

Model Scanning

Scanning Local Files

from aidefense.modelscan import ModelScanClient
from aidefense.modelscan.models import ScanStatus

# Initialize client
client = ModelScanClient(api_key="YOUR_MANAGEMENT_API_KEY")

# Scan a local model file
result = client.scan_file("/path/to/model.pkl")

# Check the results
if result.status == ScanStatus.COMPLETED:
    print("✅ Scan completed successfully")

    # Check for threats in each file
    for file_info in result.analysis_results.items:
        if file_info.threats.items:
            print(f"⚠️  Threats found in {file_info.name}:")
        else:
            print(f"✅ {file_info.name} is clean")
elif result.status == ScanStatus.FAILED:
    print("❌ Scan failed")

Scanning Repositories

from aidefense.modelscan import ModelScanClient
from aidefense.modelscan.models import (
    ModelRepoConfig, Auth, HuggingFaceAuth, URLType, ScanStatus
)

# Initialize client
client = ModelScanClient(api_key="YOUR_MANAGEMENT_API_KEY")

# Configure repository scan with authentication
repo_config = ModelRepoConfig(
    url="https://huggingface.co/username/model-name",
    type=URLType.HUGGING_FACE,
    auth=Auth(huggingface=HuggingFaceAuth(access_token="YOUR_HF_TOKEN"))
)

# Scan the repository
result = client.scan_repo(repo_config)

# Process results
if result.status == ScanStatus.COMPLETED:
    print("✅ Repository scan completed")
    print(f"Repository: {result.repository.url}")
    print(f"Files scanned: {result.repository.files_scanned}")

    # Check for threats
    for file_info in result.analysis_results.items:
        if file_info.threats.items:
            print(f"⚠️  Threats in {file_info.name}")

Listing and Managing Scans

from aidefense.modelscan import ModelScanClient
from aidefense.modelscan.models import (
    ListScansRequest, GetScanStatusRequest
)

client = ModelScanClient(api_key="YOUR_MANAGEMENT_API_KEY")

# List all scans with pagination
request = ListScansRequest(limit=10, offset=0)
response = client.list_scans(request)

print(f"Found {response.scans.paging.total} scans")
for scan in response.scans.items:
    print(f"  • {scan.scan_id}: {scan.name} - {scan.status}")

# Get detailed information about a specific scan
scan_id = response.scans.items[0].scan_id
detail_request = GetScanStatusRequest(file_limit=10, file_offset=0)
detail_response = client.get_scan(scan_id, detail_request)

scan_info = detail_response.scan_status_info
print(f"Scan status: {scan_info.status}")
print(f"Files analyzed: {len(scan_info.analysis_results.items)}")

AIBOM

from aidefense import Config
from aidefense.aibom.aibom_client import AiBomClient

client = AiBomClient(
    api_key="YOUR_MANAGEMENT_API_KEY",
    config=Config(management_base_url="https://api.security.cisco.com"),
)

# Run local AIBOM analysis, then submit it
report = client.analyze(sources=["/path/to/project"])
submission = client.submit_report_file(raw_data=report)
print(f"Created analysis: {submission.analysis_id}")

For direct endpoint-style operations (create/list/get/components/summary/delete), use the base client and examples in aidefense/aibom/README.md.

Management API

Managing Applications

from aidefense.management import ManagementClient
from aidefense.management.models.application import CreateApplicationRequest, UpdateApplicationRequest
from aidefense.management.models.connection import ConnectionType

# Initialize client
client = ManagementClient(api_key="YOUR_MANAGEMENT_API_KEY")

# Create an application
create_app_request = CreateApplicationRequest(
    application_name="My Test App",
    description="Test application created via SDK",
    connection_type=ConnectionType.API
)
result = client.applications.create_application(create_app_request)
application_id = result.application_id

# Get application details
application = client.applications.get_application(application_id, expanded=True)
print(f"Application name: {application.application_name}")

# Update an application
update_request = UpdateApplicationRequest(
    application_name="Updated App Name",
    description="Updated description"
)
client.applications.update_application(application_id, update_request)

# Delete an application
client.applications.delete_application(application_id)

Managing Policies and Connections

from aidefense.management import ManagementClient
from aidefense.management.models.policy import ListPoliciesRequest, AddOrUpdatePolicyConnectionsRequest

# Initialize client
client = ManagementClient(api_key="YOUR_MANAGEMENT_API_KEY")

# List policies
policies = client.policies.list_policies(ListPoliciesRequest(limit=10, expanded=True))
for policy in policies.items:
    print(f"{policy.policy_id}: {policy.name}")

# Associate connections with a policy
policy_id = policies.items[0].policy_id
client.policies.update_policy_connections(
    policy_id,
    AddOrUpdatePolicyConnectionsRequest(
        connections_to_associate=["connection-id-1", "connection-id-2"]
    )
)

Managing Events

from aidefense.management import ManagementClient
from aidefense.management.models.event import ListEventsRequest
from datetime import datetime, timedelta

# Initialize client
client = ManagementClient(api_key="YOUR_MANAGEMENT_API_KEY")

# List events from the last 24 hours
end_time = datetime.now()
start_time = end_time - timedelta(days=1)

list_events_request = ListEventsRequest(
    limit=5,
    start_date=start_time,
    end_date=end_time,
    expanded=True,
    sort_by="event_timestamp",
    order="desc"
)

events = client.events.list_events(list_events_request)
print(f"Found {events.paging.total} events")

# Get details for an event
if events.items:
    event_id = events.items[0].event_id
    event_detail = client.events.get_event(event_id, expanded=True)
    print(f"Event action: {event_detail.event_action}")

    # Get conversation for the event
    conversation = client.events.get_event_conversation(event_id, expanded=True)
    if "messages" in conversation and conversation["messages"].items:
        print(f"Found {len(conversation['messages'].items)} messages in conversation")

---

Configuration

The SDK uses a Config object for global settings:

• Logger: Pass a custom logger or logger parameters.
• Retry Policy: Customize retry attempts, backoff, and status codes.
• Connection Pool: Control HTTP connection pooling for performance.

from aidefense import Config

# Basic configuration
config = Config(
    logger_params={"level": "DEBUG"},
    retry_config={"total": 5, "backoff_factor": 1.0},
)

# Configuration with custom API endpoints
custom_endpoint_config = Config(
    runtime_base_url="https://custom-runtime-endpoint.example.com",
    management_base_url="https://custom-management-endpoint.example.com",
    logger_params={"level": "INFO"},
    retry_config={"total": 3, "backoff_factor": 2.0},
)

# Initialize clients with custom configuration
chat_client = ChatInspectionClient(api_key="YOUR_INSPECTION_API_KEY", config=custom_endpoint_config)
http_client = HttpInspectionClient(api_key="YOUR_INSPECTION_API_KEY", config=custom_endpoint_config)
management_client = ManagementClient(api_key="YOUR_MANAGEMENT_API_KEY", config=custom_endpoint_config)
validation_client = AiValidationClient(api_key="YOUR_MANAGEMENT_API_KEY", config=custom_endpoint_config)

---

Advanced Usage

• Custom Inspection Rules: Pass an InspectionConfig to inspection methods to enable/disable specific rules.
• Entity Types: For rules like PII/PCI/PHI, specify entity types for granular inspection.
• Override Default Entities: Pass a custom entities_map to HTTP inspection for full control.
• Utility Functions: Use aidefense.utils.to_base64_bytes to easily encode HTTP bodies for inspection.
• Async Support: (Coming soon) Planned support for async HTTP inspection.

---

Error Handling

All SDK errors derive from SDKError in exceptions.py. Specific exceptions include ValidationError (input issues) and ApiError (API/server issues).

from aidefense.exceptions import ValidationError, ApiError

try:
    client.inspect_prompt(Message(role=Role.USER, content="..."))
except ValidationError as ve:
    print("Validation error:", ve)
except ApiError as ae:
    print("API error:", ae)

---

Contributing

Contributions are welcome! Please open issues or pull requests for bug fixes, new features, or documentation improvements.

---

Support

For help or questions, please open an issue.