anomaly-infra 0.1.0

Reusable anomaly detection infrastructure for Django and Python projects

anomaly-infra

Reusable anomaly detection infrastructure for Django and Python applications.

anomaly-infra provides a small, framework-aware layer for evaluating suspicious activity, assigning risk metadata, sanitizing event payloads, persisting anomaly events, and optionally dispatching alerts. It is distributed as the anomaly-infra package and imported as anomaly_infra.

pip install anomaly-infra

The package is designed for two integration styles:

1. Existing Django projects that want middleware, ORM persistence, migrations, and Django settings-based configuration.
2. Non-Django Python projects that want the framework-agnostic anomaly evaluation service with custom event storage and alert dispatching.

This repository is a reusable library, not a standalone Django project. It intentionally does not include manage.py, URL routing, project settings, or application-specific business apps.

What anomaly_infra solves

Most applications need a consistent way to detect and record suspicious behavior without coupling security logic to a single product domain. anomaly_infra centralizes that infrastructure by providing:

• A normalized anomaly decision object.
• Rule profiles that map anomaly types to risk scores, severities, and categories.
• Safe feature-flag-gated detection, alerting, blocking, and rule-level controls.
• Sanitized event persistence that avoids storing raw secrets.
• Django middleware for generic request/response anomaly patterns.
• A framework-agnostic service that can be used in any Python codebase.

The package does not provide domain-specific enforcement for invoices, accounts, inventory, RBAC, or tenants. Those checks should live in the consuming application and call anomaly_infra when suspicious behavior is observed.

Key features

• Framework-agnostic AnomalyDetectionService for evaluating and recording anomalies.
• Django app integration via anomaly_infra.django.apps.AnomalyInfraConfig.
• Django middleware via anomaly_infra.django.middleware.RequestAnomalyMiddleware.
• Django ORM event model and packaged migration for AnomalyEvent.
• Integration with feature-flag-infra through its public Django service API.
• Configurable rule profiles with validation.
• Recursive sensitive-data masking for dictionaries, lists, tuples, and other non-string sequences.
• Safe defaults: detection, alerting, and blocking are disabled unless explicitly enabled by feature flags.
• Generic tenant/header mismatch detection without requiring a project-specific tenant model.
• Cache-backed counters for repeated failures, burst activity, and path probing.
• Optional transaction-isolated Django event persistence.

Installation

pip install anomaly-infra

For local development from this repository:

pip install -e ".[dev]"

Optional extras declared by the package:

pip install "anomaly-infra[celery]"
pip install "anomaly-infra[redis]"
pip install "anomaly-infra[dev]"

The current implementation does not include Celery tasks or Redis-specific code paths; those extras are dependency conveniences for applications that use those tools around this package.

Requirements

Runtime requirements from pyproject.toml:

• Python >=3.9
• Django >=4.2
• feature-flag-infra>=0.1.0

Although the core service is framework-agnostic, Django is currently a package dependency because this distribution includes Django models, middleware, providers, and migrations.

Core concepts

Anomaly decision

An anomaly decision is represented by anomaly_infra.types.AnomalyDecision. It is returned by AnomalyDetectionService.evaluate(...) and contains:

• anomaly_type
• category
• severity
• risk_score
• sanitized metadata
• should_alert
• should_block
• should_step_up
• action_taken
• user_message
• internal_message

Rule profiles

A rule profile maps an anomaly type to a risk score, severity, and category. Profiles are represented by anomaly_infra.types.RuleProfile or by dictionaries with the same fields.

Package defaults are defined for common anomaly types such as:

• invoice_amount_paid_tampering
• repeated_validation_failures
• repeated_forbidden_access
• cross_tenant_access_attempt
• burst_sensitive_endpoint_access
• path_probing
• invoice_total_mismatch
• invalid_invoice_status_transition
• stock_underflow_attempt
• duplicate_submission_pattern

If an unknown anomaly type is evaluated, the service uses the default rule profile: risk score 30, severity medium, category request.

Risk score

risk_score is an integer from 0 to 100.

The service uses the following built-in thresholds:

• >= 50: alert eligible when ANOMALY_ALERTING_ENABLED is enabled.
• >= 80: step-up review eligible.
• >= 100: blocking eligible when ANOMALY_BLOCKING_ENABLED is enabled.

Severity

Supported severities are:

• low
• medium
• high
• critical

Category

Supported categories are:

• request
• permission
• business

Alerting

Alerting is controlled by ANOMALY_ALERTING_ENABLED. A decision only has should_alert=True when its risk score is at least 50 and the alerting flag is enabled.

In Django, the default LoggingAlertDispatcher writes a compact sanitized warning log containing only the event id, anomaly type, severity, and risk score.

Blocking

Blocking is controlled by ANOMALY_BLOCKING_ENABLED. A decision only has should_block=True when its risk score is at least 100 and the blocking flag is enabled.

The core service returns the decision; it does not raise exceptions, return HTTP responses, or enforce business blocking by itself. Consuming applications should enforce blocking where appropriate.

Step-up review

A decision has should_step_up=True when the risk score is at least 80. Step-up review is represented in the returned decision so applications can require MFA, manual review, approval flows, or other application-specific verification.

Feature flag integration

anomaly-infra depends on the published package feature-flag-infra.

The Django service factory imports feature_flag_infra.django.service.get_feature_flags() lazily and passes that feature flag client into AnomalyDetectionService. The core service expects a feature flag client with an enabled(flag, *, user=None, default=False) method. It also supports an is_enabled(...) fallback for provider-style clients.

Required and recommended feature flags

Create these flags in feature-flag-infra for normal operation:

Flag	Purpose	Safe default used by anomaly_infra when missing
ANOMALY_DETECTION_ENABLED	Global master switch for anomaly evaluation in Django middleware and service checks.	False
ANOMALY_ALERTING_ENABLED	Enables alert dispatch for decisions with risk score >= 50.	False
ANOMALY_BLOCKING_ENABLED	Enables blocking decisions for risk score >= 100.	False
ANOMALY_RULE_BURST_ENABLED	Enables middleware burst detection on sensitive endpoint prefixes.	True after global detection is enabled
ANOMALY_RULE_PERMISSION_PROBING_ENABLED	Enables repeated forbidden-access and path-probing middleware rules.	True after global detection is enabled
ANOMALY_RULE_BUSINESS_TAMPERING_ENABLED	Reserved feature flag constant for business-tampering rules called by application code.	Not currently used by Django middleware
ANOMALY_RULE_CROSS_TENANT_ENABLED	Enables middleware tenant-header mismatch detection.	True after global detection is enabled

Recommended initial rollout:

1. Enable ANOMALY_DETECTION_ENABLED in a non-production or limited cohort.
2. Keep ANOMALY_ALERTING_ENABLED=False until event volume is understood.
3. Keep ANOMALY_BLOCKING_ENABLED=False until business enforcement paths are explicitly tested.
4. Enable or disable individual rule flags to tune middleware behavior.

Django integration

Minimal INSTALLED_APPS setup

Add both feature-flag-infra and anomaly-infra Django apps to your existing Django project:

INSTALLED_APPS = [
    # Django apps...
    "django.contrib.auth",
    "django.contrib.contenttypes",

    # Feature flags must be installed for the default Django service factory.
    "feature_flag_infra.django.apps.FeatureFlagInfraConfig",

    # anomaly-infra Django integration.
    "anomaly_infra.django.apps.AnomalyInfraConfig",
]

Confirmed Django app config path:

anomaly_infra.django.apps.AnomalyInfraConfig

Confirmed app label:

anomaly_infra

Middleware setup

Add the request anomaly middleware to MIDDLEWARE:

MIDDLEWARE = [
    # ...your existing middleware...
    "anomaly_infra.django.middleware.RequestAnomalyMiddleware",
]

The middleware calls the downstream view first, then inspects the request and response. If ANOMALY_DETECTION_ENABLED is disabled, it returns the response without running any detectors.

Migrations

anomaly-infra includes a packaged initial migration:

anomaly_infra/django/migrations/0001_initial.py

Run migrations from your Django project:

python manage.py migrate

This creates the AnomalyEvent table for the app label anomaly_infra.

Required feature flag setup

At minimum, create and enable the global detection flag in your feature flag system:

ANOMALY_DETECTION_ENABLED=True

Then enable alerting and blocking only when you are ready:

ANOMALY_ALERTING_ENABLED=True
ANOMALY_BLOCKING_ENABLED=False

The exact administration workflow depends on how your project uses feature-flag-infra.

Settings configuration

A minimal safe configuration can be as small as:

ANOMALY_TRUST_X_FORWARDED_FOR = False

A more complete configuration:

ANOMALY_API_VERSION = "api/v1"
ANOMALY_TRUST_X_FORWARDED_FOR = False

ANOMALY_SENSITIVE_PREFIXES = (
    "/api/v1/auth/",
    "/api/v1/account/",
    "/api/v1/users/",
    "/api/v1/payments/",
)

ANOMALY_PROBE_PREFIXES = (
    "/admin/",
    "/api/v1/account/roles/",
    "/api/v1/users/",
)

ANOMALY_BURST_THRESHOLD = 20
ANOMALY_BURST_WINDOW_SECONDS = 60
ANOMALY_VALIDATION_FAILURE_THRESHOLD = 5
ANOMALY_VALIDATION_FAILURE_WINDOW_SECONDS = 120
ANOMALY_PATH_PROBE_THRESHOLD = 10
ANOMALY_PATH_PROBE_WINDOW_SECONDS = 300

ANOMALY_RULE_PROFILES = {
    "cross_tenant_access_attempt": {
        "risk_score": 70,
        "severity": "high",
        "category": "permission",
    },
}

Usage in views or services

Use the Django service factory when you want to evaluate or record anomalies manually in a Django view, service, signal handler, or domain service:

from anomaly_infra.django.request import build_event_payload
from anomaly_infra.django.service import get_anomaly_service


def my_view(request):
    anomaly_service = get_anomaly_service()

    decision = anomaly_service.evaluate(
        "duplicate_submission_pattern",
        user=getattr(request, "user", None),
        metadata={"form": "checkout"},
        user_message="We need to verify this request.",
    )

    if decision.should_step_up:
        # Start your application-specific verification flow.
        pass

    payload = build_event_payload(
        decision,
        request=request,
        user=getattr(request, "user", None),
        payload={"submitted_fields": ["shipping_address", "payment_method"]},
    )
    anomaly_service.record(decision, payload=payload)

    # Continue with your normal response handling.

Usage in business logic

Business logic should evaluate application-specific anomalies directly. For example, if your application detects an invalid state transition:

from anomaly_infra.django.service import get_anomaly_service


def validate_status_transition(*, user, current_status: str, requested_status: str) -> None:
    allowed = {("draft", "submitted"), ("submitted", "approved")}
    if (current_status, requested_status) in allowed:
        return

    anomaly_service = get_anomaly_service()
    decision = anomaly_service.evaluate(
        "invalid_invoice_status_transition",
        user=user,
        metadata={
            "current_status": current_status,
            "requested_status": requested_status,
        },
        user_message="Invalid status transition.",
    )
    anomaly_service.record(
        decision,
        payload={
            "anomaly_type": decision.anomaly_type,
            "category": decision.category,
            "severity": decision.severity,
            "risk_score": decision.risk_score,
            "user": user if getattr(user, "is_authenticated", False) else None,
            "metadata": decision.metadata,
            "action_taken": decision.action_taken,
            "blocked": decision.should_block,
        },
    )

    if decision.should_block:
        raise PermissionError(decision.user_message)

How anomaly events are stored

The default Django service uses DjangoAnomalyEventStore, which creates AnomalyEvent rows through Django's ORM.

AnomalyEvent stores:

• anomaly type, category, severity, and risk score
• optional user foreign key
• generic tenant id/name fields
• request path, method, resource type, and resource id
• IP address, user agent, device id, and request id
• status code
• sanitized metadata and masked payload JSON
• action taken and blocked flag
• resolution fields (resolved, resolved_at, notes)
• timestamps

By default, DjangoAnomalyEventStore attempts to persist events outside an active business transaction by using an autocommit clone of the configured database connection. This behavior is controlled by settings documented below.

Django settings reference

Only the following Django settings are read by the current codebase.

Setting	Default	Used by	Description
ANOMALY_RULE_PROFILES	{}	Django service factory	Project overrides or additions for rule profiles. Merged with package defaults.
ANOMALY_API_VERSION	"api/v1"	Middleware	API version used to construct default sensitive and probe prefixes. Leading/trailing slashes are normalized.
ANOMALY_SENSITIVE_PREFIXES	("/api/v1/auth/", "/api/v1/account/", "/api/v1/users/", "/api/v1/invoice/", "/api/v1/payments/")	Middleware	Path prefixes considered sensitive for burst detection.
ANOMALY_PROBE_PREFIXES	("/admin/", "/api/v1/account/roles/", "/api/v1/users/")	Middleware	Path prefixes considered probe-prone for path probing detection.
ANOMALY_BURST_THRESHOLD	20	Middleware	Number of write requests to a sensitive prefix before recording burst_sensitive_endpoint_access.
ANOMALY_BURST_WINDOW_SECONDS	60	Middleware	Cache TTL/window for burst counters.
ANOMALY_VALIDATION_FAILURE_THRESHOLD	5	Middleware	Number of 400 or 403 responses by actor before recording repeated failure anomalies.
ANOMALY_VALIDATION_FAILURE_WINDOW_SECONDS	120	Middleware	Cache TTL/window for repeated validation/forbidden counters.
ANOMALY_PATH_PROBE_THRESHOLD	10	Middleware	Number of requests to the same probe path by actor before recording path_probing.
ANOMALY_PATH_PROBE_WINDOW_SECONDS	300	Middleware	Cache TTL/window for path probe counters.
ANOMALY_TRUST_X_FORWARDED_FOR	False	Request helpers	When True, get_ip() uses the first value in X-Forwarded-For; otherwise it uses REMOTE_ADDR.
ANOMALY_PERSIST_EVENTS_OUTSIDE_TRANSACTIONS	True	Django event store	When enabled, event persistence inside transaction.atomic() uses an independent autocommit database alias where possible.
ANOMALY_EVENT_DATABASE_ALIAS	None	Django event store	Optional explicit database alias for transaction-independent anomaly event writes.

Rule profile configuration

Default rule profile behavior

AnomalyConfig always starts with package defaults and then applies overrides supplied by the application.

from anomaly_infra.config import AnomalyConfig

config = AnomalyConfig()
profile = config.get_rule_profile("cross_tenant_access_attempt", default=None)

If evaluate() receives an anomaly type not present in config.rule_profiles, the service uses the package default fallback profile:

risk_score = 30
severity = "medium"
category = "request"

Override profiles in Django settings.py

ANOMALY_RULE_PROFILES = {
    "cross_tenant_access_attempt": {
        "risk_score": 75,
        "severity": "high",
        "category": "permission",
    },
    "custom_checkout_velocity": {
        "risk_score": 65,
        "severity": "high",
        "category": "business",
    },
}

The Django service factory reads these settings when get_anomaly_service() first creates its cached singleton. In tests or dynamic configuration scenarios, call anomaly_infra.django.service.reset_anomaly_service() to clear the cached service.

Configure profiles in non-Django Python

from anomaly_infra.config import AnomalyConfig
from anomaly_infra.service import AnomalyDetectionService, StaticFeatureFlags

config = AnomalyConfig(
    rule_profiles={
        "custom_rule": {
            "risk_score": 55,
            "severity": "high",
            "category": "request",
        },
    }
)

service = AnomalyDetectionService(
    flags=StaticFeatureFlags({"ANOMALY_DETECTION_ENABLED": True}),
    config=config,
)

Validation rules

Invalid rule profile configuration raises immediately during AnomalyConfig(...) construction.

• risk_score must be an int from 0 through 100.
• severity must be one of low, medium, high, or critical.
• category must be one of request, permission, or business.
• Profile names must be non-empty strings.
• Each dictionary profile must include risk_score, severity, and category.

Non-Django Python usage

The framework-agnostic API lives under anomaly_infra.config, anomaly_infra.service, anomaly_infra.interfaces, and anomaly_infra.types.

AnomalyDetectionService

AnomalyDetectionService evaluates anomaly types against configured rule profiles and records sanitized events through an optional event store.

Important methods:

• is_enabled(user=None) -> bool
• flag_enabled(flag_name, user=None, default=False) -> bool
• evaluate(anomaly_type, metadata=None, user=None, user_message="...", internal_message="...") -> AnomalyDecision
• record(decision, payload=None) -> Any

Custom AnomalyEventStore

Implement anomaly_infra.interfaces.AnomalyEventStore or provide any object with a compatible save(payload: dict) method. The service passes a sanitized payload to save().

Custom AlertDispatcher

Implement anomaly_infra.interfaces.AlertDispatcher or provide any object with a compatible dispatch(event_id: str, payload: dict | None = None) method. The dispatcher is called only when decision.should_alert is True.

FeatureFlagService usage

For non-Django applications, pass a feature flag client that supports:

enabled(flag: str, *, user=None, default: bool = False) -> bool

This can be a client from feature-flag-infra, a wrapper around your own flag system, or StaticFeatureFlags for simple setups and tests.

Complete copy-paste example

from dataclasses import dataclass

from anomaly_infra.config import AnomalyConfig
from anomaly_infra.constants import (
    ANOMALY_ALERTING_ENABLED,
    ANOMALY_DETECTION_ENABLED,
)
from anomaly_infra.service import AnomalyDetectionService, StaticFeatureFlags


@dataclass
class StoredEvent:
    id: str
    payload: dict


class InMemoryAnomalyEventStore:
    def __init__(self):
        self.events = []

    def save(self, payload: dict):
        event = StoredEvent(id=f"event-{len(self.events) + 1}", payload=payload)
        self.events.append(event)
        return event


class PrintAlertDispatcher:
    def dispatch(self, event_id: str, payload: dict | None = None):
        print(f"alert dispatched for {event_id}: {payload.get('anomaly_type')}")


flags = StaticFeatureFlags(
    {
        ANOMALY_DETECTION_ENABLED: True,
        ANOMALY_ALERTING_ENABLED: True,
    }
)
store = InMemoryAnomalyEventStore()

service = AnomalyDetectionService(
    flags=flags,
    event_store=store,
    alert_dispatcher=PrintAlertDispatcher(),
    config=AnomalyConfig(
        rule_profiles={
            "custom_login_velocity": {
                "risk_score": 60,
                "severity": "high",
                "category": "request",
            },
        }
    ),
)

if service.is_enabled():
    decision = service.evaluate(
        "custom_login_velocity",
        metadata={
            "username": "[e-mail]",
            "access_token": "secret-token-that-will-be-masked",
            "attempt_count": 8,
        },
        user_message="Additional verification is required.",
    )

    event = service.record(
        decision,
        payload={
            "anomaly_type": decision.anomaly_type,
            "category": decision.category,
            "severity": decision.severity,
            "risk_score": decision.risk_score,
            "metadata": decision.metadata,
            "masked_payload": {
                "password": "raw secret will be masked",
                "safe_field": "safe value",
            },
            "action_taken": decision.action_taken,
            "blocked": decision.should_block,
        },
    )

    print(event.id)
    print(event.payload)

Manual anomaly recording example

Manual recording is appropriate when application code detects a domain-specific condition that middleware cannot know about.

from anomaly_infra.constants import ANOMALY_DETECTION_ENABLED
from anomaly_infra.service import AnomalyDetectionService, StaticFeatureFlags

service = AnomalyDetectionService(
    flags=StaticFeatureFlags({ANOMALY_DETECTION_ENABLED: True}),
)

decision = service.evaluate(
    "stock_underflow_attempt",
    metadata={"sku": "SKU-001", "requested_quantity": 25, "available_quantity": 3},
    user_message="Requested quantity is not available.",
)

if decision.should_block:
    # Enforce your application-specific block here.
    pass

# Without an event_store, record() logs that no store is configured and returns None.
service.record(decision, payload={"metadata": decision.metadata})

In production, pass an event store so record() can persist the sanitized event.

Middleware behavior

RequestAnomalyMiddleware is intentionally generic and framework-isolated. It does not import project-specific apps and does not require a tenant model.

The middleware detects the following patterns after the response is produced:

1. Tenant header mismatch

   • Rule: cross_tenant_access_attempt
   • Controlled by: ANOMALY_RULE_CROSS_TENANT_ENABLED
   • Compares X-Tenant or X-Tenant-Id headers with request.tenant.id, request.tenant.name, or request.tenant_id when available.

2. Repeated validation failures

   • Rule: repeated_validation_failures
   • Triggered by repeated 400 responses from the same actor.
   • Threshold/window: ANOMALY_VALIDATION_FAILURE_THRESHOLD and ANOMALY_VALIDATION_FAILURE_WINDOW_SECONDS.

3. Repeated forbidden access

   • Rule: repeated_forbidden_access
   • Controlled by: ANOMALY_RULE_PERMISSION_PROBING_ENABLED
   • Triggered by repeated 403 responses from the same actor.
   • Threshold/window: ANOMALY_VALIDATION_FAILURE_THRESHOLD and ANOMALY_VALIDATION_FAILURE_WINDOW_SECONDS.

4. Burst sensitive endpoint access

   • Rule: burst_sensitive_endpoint_access
   • Controlled by: ANOMALY_RULE_BURST_ENABLED
   • Applies to POST, PUT, PATCH, and DELETE requests whose path starts with one of ANOMALY_SENSITIVE_PREFIXES.
   • Threshold/window: ANOMALY_BURST_THRESHOLD and ANOMALY_BURST_WINDOW_SECONDS.

5. Path probing

   • Rule: path_probing
   • Controlled by: ANOMALY_RULE_PERMISSION_PROBING_ENABLED
   • Applies to paths that start with one of ANOMALY_PROBE_PREFIXES.
   • Threshold/window: ANOMALY_PATH_PROBE_THRESHOLD and ANOMALY_PATH_PROBE_WINDOW_SECONDS.

Actor counters use the authenticated user.id when available; otherwise they use the detected IP address or anon.

The global detection flag disables all middleware detectors:

ANOMALY_DETECTION_ENABLED=False

When the global flag is disabled or missing, middleware returns the response without recording events.

Security model

Sensitive data masking

mask_sensitive() recursively masks values for keys that contain sensitive fragments. Matching is case-insensitive and normalizes hyphens to underscores.

Sensitive fragments include:

password, token, refresh, access, authorization, secret, api_key,
card, cvv, pin, cookie, session, csrf, set-cookie

Masked values are replaced with:

***

No raw secret persistence

AnomalyDetectionService.record() sanitizes payloads before saving. It removes top-level payload and raw_payload keys and re-sanitizes metadata and masked_payload when present.

The Django request payload builder stores sanitized metadata and masked_payload; it does not persist raw request bodies.

X-Forwarded-For trust setting

By default, ANOMALY_TRUST_X_FORWARDED_FOR=False, so IP extraction uses REMOTE_ADDR.

Set this to True only if your application is behind a trusted proxy that correctly sets and sanitizes X-Forwarded-For:

ANOMALY_TRUST_X_FORWARDED_FOR = True

Framework isolation

The core service has no Django dependency in its import path. Django-specific code lives under anomaly_infra.django.

The Django middleware is generic and avoids imports from consuming project apps.

Safe defaults

• Global anomaly detection defaults to disabled.
• Alerting defaults to disabled.
• Blocking defaults to disabled.
• Rule-level middleware flags default to enabled only after global detection is enabled.
• Payloads are sanitized before event persistence and alert logging.
• Forwarded IP headers are ignored unless explicitly trusted.

Testing

Install development dependencies, then run:

pytest

The repository also supports the pytest configuration in pytest.ini, which sets DJANGO_SETTINGS_MODULE=tests.settings and uses pytest-django.

Current test coverage areas include:

• Rule profile defaults, overrides, and validation.
• Decision thresholds for alerting, blocking, and step-up review.
• Feature flag behavior and safe fallbacks.
• Payload and metadata sanitization.
• Django app configuration and migrations.
• Django model persistence and mark_resolved() behavior.
• Request IP extraction and X-Forwarded-For trust behavior.
• Middleware detection for disabled global detection, repeated 400/403 responses, sensitive endpoint bursts, tenant mismatch, path probing, anonymous users, and non-standard responses.
• Transaction-independent Django event persistence.

Useful packaging commands from the Makefile:

make clean
make build

Package structure

anomaly_infra/
  __init__.py
  config.py                  # AnomalyConfig and rule profile validation
  constants.py               # Feature flag names, categories, severities, thresholds
  defaults.py                # Bundled default rule profiles
  interfaces.py              # AnomalyEventStore and AlertDispatcher interfaces
  sanitizer.py               # Recursive sensitive-data masking
  service.py                 # Framework-agnostic AnomalyDetectionService
  types.py                   # RuleProfile and AnomalyDecision dataclasses
  django/
    __init__.py
    apps.py                  # AnomalyInfraConfig; app label anomaly_infra
    counters.py              # Cache-backed counter helpers
    middleware.py            # RequestAnomalyMiddleware
    migrations/
      0001_initial.py        # AnomalyEvent migration
    models.py                # AnomalyEvent ORM model
    providers.py             # DjangoAnomalyEventStore and LoggingAlertDispatcher
    request.py               # Request metadata/IP/payload helpers
    service.py               # get_anomaly_service() factory

tests/
  conftest.py
  settings.py
  test_core.py
  test_debug_settings.py
  test_django_integration.py
  test_sanitizer.py

Public APIs documented

Core APIs:

• anomaly_infra.config.AnomalyConfig
• anomaly_infra.service.AnomalyDetectionService
• anomaly_infra.service.StaticFeatureFlags
• anomaly_infra.interfaces.AnomalyEventStore
• anomaly_infra.interfaces.AlertDispatcher
• anomaly_infra.types.RuleProfile
• anomaly_infra.types.AnomalyDecision
• anomaly_infra.sanitizer.mask_sensitive

Django APIs:

• anomaly_infra.django.apps.AnomalyInfraConfig
• anomaly_infra.django.middleware.RequestAnomalyMiddleware
• anomaly_infra.django.models.AnomalyEvent
• anomaly_infra.django.providers.DjangoAnomalyEventStore
• anomaly_infra.django.providers.LoggingAlertDispatcher
• anomaly_infra.django.request.get_ip
• anomaly_infra.django.request.get_user_agent
• anomaly_infra.django.request.request_meta
• anomaly_infra.django.request.build_event_payload
• anomaly_infra.django.service.get_anomaly_service
• anomaly_infra.django.service.reset_anomaly_service

Roadmap

The following items are not implemented in the current codebase but are natural future extensions:

• First-class documentation examples for specific feature-flag-infra admin workflows.
• Pluggable alert dispatchers for email, Slack, webhooks, or task queues.
• Optional Celery task helpers for asynchronous alert delivery.
• Optional Redis-specific counter backend guidance and operational tuning.
• Management commands for anomaly event cleanup, export, or reporting.
• Admin UI helpers for reviewing and resolving anomaly events.
• Additional framework integrations beyond Django.
• More built-in rule profiles and detector helpers driven by real-world application patterns.

License

MIT License. See LICENSE.