AI Security

Security controls designed for AI systems

AI introduces unique risks that traditional security frameworks don't address. Lane 2 of the AI SecureOps Framework provides comprehensive coverage for AI-specific threats.

Threat Modeling
Prompt Security
Model Governance

Risk Categories

Understanding AI-specific risks

AI systems face unique risks across data, models, interactions, and infrastructure. The framework addresses each category with specific controls.

Data & Training Risk

Risks related to training data quality, provenance, bias, and privacy.

RiskImpactMitigation
Training data poisoningModel produces incorrect or malicious outputsData provenance tracking, validation pipelines
PII in training dataPrivacy violations, regulatory exposureData classification, PII detection, redaction
Biased training dataDiscriminatory outputs, reputational damageBias detection, diverse data sources, monitoring
Data leakage in outputsExposure of confidential training dataOutput filtering, membership inference testing

Model Risk

Risks related to model behavior, integrity, and operational characteristics.

RiskImpactMitigation
Model theft/extractionIP loss, competitive disadvantageAccess controls, rate limiting, watermarking
Model driftDegraded performance, incorrect outputsDrift detection, monitoring, retraining triggers
Adversarial inputsModel manipulation, incorrect outputsInput validation, adversarial training, monitoring
Unexplainable behaviorRegulatory issues, trust erosionExplainability tooling, human oversight

Prompt & Interaction Risk

Risks specific to prompt-based AI systems and user interactions.

RiskImpactMitigation
Prompt injectionUnauthorized actions, data exposureInput sanitization, privilege separation
JailbreakingBypass of safety controlsMulti-layer guardrails, monitoring, red teaming
Context manipulationModel behaves unexpectedlyContext validation, token limits, sandboxing
Social engineering via AIUsers manipulated through AI responsesOutput filtering, disclosure requirements

Infrastructure Risk

Risks related to AI infrastructure, deployment, and operations.

RiskImpactMitigation
API abuseService degradation, cost overrunsRate limiting, authentication, monitoring
Supply chain compromiseMalicious dependencies, backdoorsDependency scanning, vendor assessment
Compute resource attacksTraining/inference disruptionResource isolation, access controls
Model serving vulnerabilitiesUnauthorized access, data exposureSecure deployment, network segmentation

Methodology

AI threat modeling process

Systematic threat modeling identifies and prioritizes risks before they become incidents. The framework includes AI-specific threat modeling templates and guidance.

1

Identify Assets

Document AI components: models, training data, inference endpoints, feature stores, and customer data flows.

2

Define Threat Actors

Identify potential adversaries: external attackers, malicious users, insider threats, competitors.

3

Map Attack Surfaces

Document entry points: APIs, user inputs, data pipelines, admin interfaces, third-party integrations.

4

Enumerate Threats

Systematically identify threats using AI-specific threat frameworks (ATLAS, OWASP ML Top 10).

5

Assess & Prioritize

Score threats by likelihood and impact. Prioritize based on risk appetite and business context.

6

Define Mitigations

Document specific controls for prioritized threats. Assign ownership and implementation timeline.

Threat Modeling Outputs

Data flow diagrams for AI pipelines
Attack tree documentation
Risk register with AI-specific entries
Mitigation ownership matrix
Testing requirements per threat
Annual review schedule

Core Controls

Prompt injection and misuse prevention

LLM and prompt-based systems require specific controls to prevent injection attacks, jailbreaks, and misuse.

Input Validation

Sanitize and validate all user inputs before processing

  • Length and format validation
  • Special character filtering
  • Semantic analysis for injection patterns
  • Input type enforcement

Privilege Separation

Separate user context from system context

  • Clear boundary between user and system prompts
  • Role-based prompt access
  • Sandboxed execution environments
  • Least privilege for AI actions

Output Filtering

Filter and validate AI outputs before delivery

  • Content classification
  • PII detection and redaction
  • Harmful content filtering
  • Output format validation

Monitoring & Detection

Continuous monitoring for abuse and attacks

  • Anomaly detection in inputs/outputs
  • Jailbreak attempt detection
  • Usage pattern analysis
  • Real-time alerting
Governance

AI model governance

Models are critical assets that require governance throughout their lifecycle. The framework establishes controls for model development, deployment, monitoring, and retirement.

Model Lifecycle Stages

1

Development

Data governance, secure training, version control

2

Validation

Security testing, bias evaluation, performance

3

Deployment

Secure deployment, access controls, monitoring

4

Operation

Drift detection, incident response, updates

5

Retirement

Secure decommissioning, data retention, archival

Standards

Aligned with industry frameworks

The AI SecureOps Framework incorporates guidance from leading AI security and risk management standards.

NIST AI RMF

NIST AI Risk Management Framework provides structure for managing AI risks across the lifecycle.

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OWASP ML Top 10

Top 10 security risks specific to machine learning systems.

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MITRE ATLAS

Adversarial Threat Landscape for AI Systems - knowledge base of AI attack techniques.

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Secure your AI systems

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