Security Controls & Mitigations

Comprehensive security controls and mitigations for agentic AI systems, organized by category with implementation guidance and developer resources.

6
Control Categories
18
Total Controls
3
In Progress
3
Implemented

Access Control & Authentication

Identity & Access Management

Comprehensive access control mechanisms for AI systems and data

Priority: CriticalImplementation: HighStatus: Implemented

Multi-Factor Authentication (MFA)

Require multiple authentication factors for AI system access

Implementation

Implement OAuth 2.0 with TOTP or hardware tokens

Tools & Technologies

Auth0OktaDuoGoogle Authenticator

Code Example

Use JWT tokens with refresh token rotation

Role-Based Access Control (RBAC)

Define and enforce role-based permissions for AI system components

Implementation

Create granular roles for different AI system functions

Tools & Technologies

AWS IAMAzure ADKubernetes RBAC

Code Example

Implement middleware for role validation

API Key Management

Secure management of API keys for AI service access

Implementation

Use secure key storage with rotation policies

Tools & Technologies

HashiCorp VaultAWS Secrets ManagerAzure Key Vault

Code Example

Implement key rotation with versioning

Data Protection & Privacy

Data Security

Protect sensitive data used in AI training and inference

Priority: CriticalImplementation: HighStatus: In Progress

Data Encryption at Rest

Encrypt all AI training data and model artifacts

Implementation

Use AES-256 encryption for stored data

Tools & Technologies

AWS KMSAzure Key VaultGoogle Cloud KMS

Code Example

Implement transparent data encryption (TDE)

Data Encryption in Transit

Encrypt data during transmission between AI system components

Implementation

Use TLS 1.3 for all communications

Tools & Technologies

Let's EncryptAWS Certificate ManagerCloudflare

Code Example

Enforce HTTPS with HSTS headers

Differential Privacy

Implement privacy-preserving techniques for AI training

Implementation

Add noise to training data to protect individual privacy

Tools & Technologies

TensorFlow PrivacyPyTorch OpacusIBM Differential Privacy

Code Example

Use Laplace noise for privacy budget management

Input Validation & Sanitization

Application Security

Validate and sanitize all inputs to AI systems

Priority: HighImplementation: MediumStatus: Implemented

Prompt Injection Prevention

Prevent prompt injection attacks against language models

Implementation

Implement input validation and prompt engineering

Tools & Technologies

OWASP ZAPSemgrepCustom validation rules

Code Example

Use prompt templates with input sanitization

Input Size Limits

Enforce reasonable limits on input size and complexity

Implementation

Set maximum token limits and input validation

Tools & Technologies

Rate limitingInput validation libraries

Code Example

Implement token counting and size validation

Content Filtering

Filter inappropriate or malicious content from inputs

Implementation

Use content moderation APIs and custom filters

Tools & Technologies

OpenAI ModerationPerspective APICustom ML models

Code Example

Implement content classification pipeline

Model Security & Robustness

AI Security

Protect AI models from attacks and ensure robustness

Priority: HighImplementation: MediumStatus: Planning

Adversarial Training

Train models to be robust against adversarial attacks

Implementation

Incorporate adversarial examples in training

Tools & Technologies

CleverHansAdversarial Robustness ToolboxCustom attacks

Code Example

Implement FGSM and PGD adversarial training

Model Watermarking

Embed watermarks in models to detect unauthorized use

Implementation

Add imperceptible watermarks to model outputs

Tools & Technologies

Custom watermarking algorithmsDigital watermarking libraries

Code Example

Implement backdoor-based watermarking

Model Monitoring

Monitor model behavior for anomalies and attacks

Implementation

Track model performance and detect drift

Tools & Technologies

MLflowWeights & BiasesCustom monitoring

Code Example

Implement drift detection algorithms

Infrastructure Security

DevOps Security

Secure the infrastructure supporting AI systems

Priority: HighImplementation: HighStatus: Implemented

Container Security

Secure containers running AI workloads

Implementation

Use minimal base images and security scanning

Tools & Technologies

Docker ScoutTrivySnykClair

Code Example

Implement multi-stage builds with security scanning

Network Security

Implement network segmentation and monitoring

Implementation

Use VPCs, firewalls, and network monitoring

Tools & Technologies

AWS VPCAzure NSGKubernetes Network Policies

Code Example

Implement network policies for pod communication

Secrets Management

Secure management of secrets and credentials

Implementation

Use dedicated secrets management services

Tools & Technologies

HashiCorp VaultAWS Secrets ManagerAzure Key Vault

Code Example

Implement secrets injection at runtime

Monitoring & Logging

Observability

Comprehensive monitoring and logging for AI systems

Priority: MediumImplementation: MediumStatus: In Progress

Audit Logging

Log all AI system activities for audit purposes

Implementation

Implement structured logging with correlation IDs

Tools & Technologies

ELK StackSplunkDatadogCustom logging

Code Example

Use structured JSON logging with correlation IDs

Performance Monitoring

Monitor AI system performance and resource usage

Implementation

Track metrics for model performance and infrastructure

Tools & Technologies

PrometheusGrafanaNew RelicDatadog

Code Example

Implement custom metrics for model inference time

Security Event Monitoring

Monitor for security events and potential attacks

Implementation

Use SIEM tools to correlate security events

Tools & Technologies

SplunkELK StackAzure SentinelAWS GuardDuty

Code Example

Implement security event correlation rules

🔧Implementation Resources

Security Tools

AI Security Frameworks

Developer Resources