Traditional security frameworks weren't designed for AI. When your application can be manipulated with natural language, when your data includes model weights worth millions, when attackers can extract capabilities through careful questioning—you need a different approach.
This guide walks through building an AI security framework from first principles: understanding the threats, designing controls, and implementing defenses.
The AI Threat Landscape
AI systems have attack surfaces that traditional applications don't. Let's map them:
Prompt Injection
Attackers craft inputs that cause the AI to ignore its instructions and follow the attacker's commands instead. This can leak data, bypass controls, or cause unauthorized actions.
Attack Vector: User inputs, retrieved documents, tool outputs—any text the model processes
Impact: Data exfiltration, unauthorized actions, system compromise
Data Poisoning
Attackers inject malicious data into training sets or retrieval databases, causing the model to behave incorrectly when triggered.
Attack Vector: Training data, RAG knowledge bases, fine-tuning datasets
Impact: Backdoors, biased outputs, reliability degradation
Model Extraction
Attackers query the model systematically to recreate its capabilities or extract proprietary knowledge encoded in fine-tuning.
Attack Vector: API access, repeated queries
Impact: IP theft, loss of competitive advantage
Training Data Extraction
Attackers craft prompts that cause the model to regurgitate sensitive data from its training set.
Attack Vector: Carefully crafted prompts
Impact: Privacy violations, data breach
Denial of Service
Attackers send requests designed to consume maximum resources—long contexts, complex reasoning, recursive tool calls.
Attack Vector: API access
Impact: Service degradation, cost inflation
Building Your Security Framework
Layer 1: Perimeter Controls
These are your first line of defense—preventing malicious inputs from reaching the AI system at all.
Input Validation
- Length limits on all inputs
- Character set restrictions
- Format validation (when applicable)
- Rate limiting per user/session
Authentication and Authorization
- Strong user authentication
- Role-based access to AI features
- API key management and rotation
- Session management
Layer 2: AI-Specific Controls
These controls address threats unique to AI systems.
Prompt Injection Defense
- Input scanning for injection patterns
- Clear delimiter separation between instructions and user data
- Output validation before action execution
- Instruction hierarchy enforcement
Context Management
- Strict separation of system prompts from user inputs
- Document source tracking in RAG systems
- Context window management
- Memory isolation between users
Output Guardrails
- PII detection and redaction
- Content policy enforcement
- Hallucination detection
- System prompt leak prevention
Layer 3: Action Controls
For AI agents that take actions, additional controls are critical.
Tool and API Security
- Principle of least privilege for tool access
- Input validation on tool parameters
- Output sanitization from tools
- Allowlisting permitted actions
Transaction Controls
- Value-based thresholds requiring human approval
- Irreversible action confirmation
- Anomaly detection on agent behavior
- Kill switches and circuit breakers
Layer 4: Data Security
Protecting the data that powers your AI.
Training and Fine-tuning Data
- Data provenance tracking
- Anomaly detection in training data
- Access controls on training pipelines
- Version control and rollback capability
RAG Knowledge Bases
- Document validation before ingestion
- Source authentication
- Regular audits for poisoned content
- Access logging
Layer 5: Monitoring and Response
Detection and response when prevention fails.
Logging and Observability
- Full request/response logging
- Guardrail trigger logging
- User behavior analytics
- Model output drift detection
Incident Response
- AI-specific incident response procedures
- Model rollback capability
- Emergency shutdown procedures
- Communication templates
Implementing Defense in Depth
Prime AI Guardrails provides ready-made controls for Layers 2-5 of this framework—prompt injection defense, output guardrails, action controls, and comprehensive monitoring. Deploy a complete security stack without building from scratch.
Mapping Controls to OWASP LLM Top 10
The OWASP Top 10 for LLM Applications provides a useful checklist:
- LLM01: Prompt Injection → Input scanning, delimiter separation, output validation
- LLM02: Insecure Output Handling → Output encoding, validation before use
- LLM03: Training Data Poisoning → Data provenance, anomaly detection
- LLM04: Model Denial of Service → Rate limiting, resource quotas
- LLM05: Supply Chain Vulnerabilities → Model and dependency verification
- LLM06: Sensitive Information Disclosure → PII detection, output filtering
- LLM07: Insecure Plugin Design → Tool validation, least privilege
- LLM08: Excessive Agency → Action controls, human-in-the-loop
- LLM09: Overreliance → Confidence calibration, uncertainty communication
- LLM10: Model Theft → Access controls, query monitoring
Implementation Priorities
You can't implement everything at once. Here's a pragmatic ordering:
Phase 1: Foundation (Week 1-2)
- Basic input validation and rate limiting
- Authentication/authorization
- Request logging
- PII detection on outputs
Phase 2: AI-Specific (Week 3-4)
- Prompt injection detection
- Content policy enforcement
- System prompt protection
- Guardrail monitoring dashboards
Phase 3: Advanced (Month 2)
- Action controls for agents
- Hallucination detection
- Anomaly detection
- Incident response procedures
Phase 4: Mature (Ongoing)
- Red team exercises
- Continuous improvement
- Threat intelligence integration
- Compliance automation
Measuring Security Posture
Track these metrics to assess your AI security:
- Injection attempt rate: How often are users attempting prompt injection?
- Block rate: What percentage of attempts are caught?
- False positive rate: How often do guardrails block legitimate requests?
- Mean time to detect: How quickly do you identify new attack patterns?
- Coverage: What percentage of AI interactions pass through guardrails?
AI security isn't a destination—it's an ongoing practice. Attackers evolve, models change, and new vulnerabilities emerge. The organizations that treat AI security as a continuous program, not a one-time project, are the ones that avoid becoming headlines.