Adaptive Defense Agent (ADA): Automated Moving Target Defense for AI Workloads

Introduction

AI workloads have a different threat profile than “regular” microservices: they’re high-value targets (models, prompts, data), often exposed via public-ish APIs, and they tend to accumulate complex dependencies. Defenders don’t always get the benefit of clean, easily patchable application layers.

Adaptive Defense Agent (ADA), introduced in May 2025, applies an Automated Moving Target Defense (AMTD) strategy to Kubernetes: it continuously rotates AI workloads and related infrastructure signals to reduce attacker dwell time and disrupt reconnaissance.

What to sanity-check before adopting

• State and warm caches: frequent rotation can fight against model warmup and cache locality—measure the latency impact.
• Secrets and identity churn: rotating configs/secrets helps, but make sure your downstream systems tolerate it.
• SLO-driven schedules: align rotation frequency with business hours and traffic patterns (not just “every N minutes”).
• Detection + response integration: the best value is event-driven rotation tied to real signals, not constant churn.

Moving Target Defense

• Continuous rotation automatically rotates AI workload pods and their infrastructure components on a regular schedule.
• Attack surface disruption disrupts reconnaissance and attack attempts by constantly changing target characteristics.
• Ephemeral infrastructure leverages Kubernetes pod ephemerality to create dynamic, constantly changing environments.
• Unpredictable patterns implements unpredictable rotation patterns that are difficult for attackers to predict or exploit.

AI Workload Protection

1. Model protection protects machine learning models from adversarial attacks through frequent rotation.
2. Inference security secures AI inference endpoints by rotating serving pods and their configurations.
3. Training protection protects training workloads from data poisoning and model theft attacks.
4. API security secures AI service APIs by rotating endpoints and access credentials regularly.

Kubernetes-Native Implementation

• Pod rotation automatically rotates pods running AI workloads based on configurable schedules and triggers.
• Configuration rotation rotates configurations, secrets, and environment variables associated with AI workloads.
• Network rotation changes network identities, IP addresses, and service endpoints to disrupt network-based attacks.
• Namespace isolation uses namespace rotation and isolation for enhanced security boundaries.

Automated Rotation Strategies

• Time-based rotation rotates workloads on fixed time intervals (e.g., every hour, daily).
• Event-based rotation triggers rotation based on security events, anomalies, or threat indicators.
• Load-based rotation rotates during low-traffic periods to minimize service disruption.
• Adaptive rotation adjusts rotation frequency based on threat levels and security metrics.

Zero-Downtime Rotation

• Rolling updates uses Kubernetes rolling update strategies to maintain service availability during rotation.
• Traffic migration seamlessly migrates traffic from old to new pod instances without interruption.
• State preservation preserves model state and inference data during rotation for continuity.
• Health checks validates new instances before routing traffic to ensure service quality.

Security Benefits

• Reconnaissance disruption makes it extremely difficult for attackers to map and target specific instances.
• Exploit window reduction minimizes the window of opportunity for successful exploits.
• Persistence prevention prevents attackers from maintaining persistent access to compromised workloads.
• Attack complexity increases the complexity and cost of successful attacks significantly.

Use Cases

• Production AI services protects production AI inference services from targeted attacks.
• Model serving secures ML model serving endpoints in multi-tenant environments.
• Training pipelines protects sensitive training workloads and model development environments.
• AI-powered applications enhances security for applications that integrate AI capabilities.

Getting Started

# Install ADA operator
kubectl apply -f https://github.com/ada/adaptive-defense/releases/latest/download/install.yaml

# Create a namespace for AI workloads
kubectl create namespace ai-production

# Define an ADA rotation policy
cat <<EOF | kubectl apply -f -
apiVersion: security.ada.io/v1alpha1
kind: RotationPolicy
metadata:
  name: ml-model-rotation
  namespace: ai-production
spec:
  selector:
    matchLabels:
      app: ml-model-serving
  strategy:
    type: time-based
    interval: 3600s  # Rotate every hour
  rotation:
    pods: true
    configs: true
    secrets: true
    networks: true
  zeroDowntime: true
  healthCheck:
    enabled: true
    timeout: 30s
EOF

Deploy an AI workload with ADA protection:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: ml-model-serving
  namespace: ai-production
  labels:
    app: ml-model-serving
    ada.protected: "true"
spec:
  replicas: 3
  selector:
    matchLabels:
      app: ml-model-serving
  template:
    metadata:
      labels:
        app: ml-model-serving
    spec:
      containers:
      - name: model-server
        image: my-registry/ml-model:latest
        ports:
        - containerPort: 8080
---
apiVersion: security.ada.io/v1alpha1
kind: RotationPolicy
metadata:
  name: auto-rotation
  namespace: ai-production
spec:
  selector:
    matchLabels:
      app: ml-model-serving
  strategy:
    type: adaptive
    baseInterval: 1800s
    threatMultiplier: 0.5  # Rotate 2x faster under threat

Monitoring and Metrics

• Rotation events tracks rotation events, schedules, and success rates.
• Security metrics monitors threat indicators and security event frequencies.
• Performance impact measures the performance impact of rotation on AI workload latency.
• Cost analysis tracks resource costs associated with rotation operations.

Integration with Security Tools

• Threat detection integrates with threat detection systems to trigger adaptive rotation.
• SIEM integration sends rotation events and security metrics to SIEM systems.
• Vulnerability scanning triggers rotation when vulnerabilities are detected.
• Incident response automatically rotates workloads during security incidents.

Summary

Adaptive Defense Agent addresses the unique security challenges of AI workloads on Kubernetes, providing an innovative moving target defense approach that makes AI services significantly more secure while maintaining availability and performance.