Security and Governance Framework

Securing Agentic AI Systems

Document ID: C3-SECURITY | Version: 1.0 | Category: P3 - Technical Deep Dives

---

Executive Summary

Agentic systems introduce unique security challenges: autonomous action, tool access, and persistent memory. This guide provides security patterns for production deployment.

---

Threat Model

Agent-Specific Threats

Threat	Description	Impact	Mitigation
Prompt Injection	Malicious input manipulation	Unauthorized actions	Input validation
Tool Abuse	Misuse of tool capabilities	Data breach, damage	Least privilege
Memory Poisoning	Corrupted memory retrieval	Wrong decisions	Memory validation
Jailbreaking	Bypass safety constraints	Policy violations	Defense in depth
Data Exfiltration	Unauthorized data access	Privacy breach	Access controls

---

Security Controls

Input Validation

class InputValidator:
    """Validate and sanitize agent inputs."""
    
    INJECTION_PATTERNS = [
        r"ignore previous instructions",
        r"disregard .* instructions",
        r"you are now",
        r"new persona",
        r"system prompt",
        r"</?(system|user|assistant)>"
    ]
    
    def validate(self, input_text: str) -> tuple[bool, str]:
        # Check for injection patterns
        for pattern in self.INJECTION_PATTERNS:
            if re.search(pattern, input_text, re.IGNORECASE):
                return False, f"Potential injection detected: {pattern}"
        
        # Length limits
        if len(input_text) > 100000:
            return False, "Input exceeds maximum length"
        
        # Encoding validation
        try:
            input_text.encode('utf-8')
        except UnicodeError:
            return False, "Invalid encoding"
        
        return True, "Valid"

Tool Access Control

class ToolAccessControl:
    """Role-based tool access control."""
    
    def __init__(self):
        self.role_permissions = {
            "reader": ["knowledge_search", "web_search"],
            "writer": ["knowledge_search", "web_search", "file_write"],
            "admin": ["*"]
        }
        
        self.tool_risk_levels = {
            "knowledge_search": "low",
            "web_search": "low",
            "file_write": "medium",
            "database_write": "high",
            "api_call": "high",
            "code_execute": "critical"
        }
    
    def check_permission(self, user_role: str, tool_name: str) -> bool:
        allowed = self.role_permissions.get(user_role, [])
        return "*" in allowed or tool_name in allowed
    
    def requires_approval(self, tool_name: str, user_role: str) -> bool:
        risk = self.tool_risk_levels.get(tool_name, "high")
        
        if risk == "critical":
            return True
        if risk == "high" and user_role != "admin":
            return True
        return False

Memory Security

class SecureMemory:
    """Secure memory with encryption and access control."""
    
    def __init__(self, encryption_key: bytes):
        from cryptography.fernet import Fernet
        self.cipher = Fernet(encryption_key)
    
    def encrypt_memory(self, content: str) -> bytes:
        return self.cipher.encrypt(content.encode())
    
    def decrypt_memory(self, encrypted: bytes) -> str:
        return self.cipher.decrypt(encrypted).decode()
    
    def validate_memory(self, content: str) -> bool:
        # Check for injection in memory content
        validator = InputValidator()
        is_valid, _ = validator.validate(content)
        return is_valid

---

Governance Framework

Policy Enforcement

Policy	Enforcement	Monitoring
Data retention	Automatic purge	Retention audit
Access logging	All actions logged	Log review
Model usage	Approved models only	Usage tracking
Cost limits	Budget enforcement	Spend alerts
Quality gates	Review requirements	Approval tracking

Audit Requirements

class AuditLogger:
    """Immutable audit logging."""
    
    def log_action(
        self,
        user_id: str,
        agent_id: str,
        action: str,
        target: str,
        result: str
    ):
        record = {
            "timestamp": datetime.utcnow().isoformat(),
            "user_id": user_id,
            "agent_id": agent_id,
            "action": action,
            "target": target,
            "result": result,
            "hash": self._compute_hash(...)
        }
        
        # Write to immutable store
        self.store.append(record)

---

Compliance Mapping

Framework	Requirements	Agent Implementation
SOC 2	Access controls	RBAC, audit logs
GDPR	Data protection	Encryption, retention
HIPAA	PHI security	Isolation, audit
PCI DSS	Cardholder data	Tokenization, access

---

Quick Reference

Control	Implementation	Verification
Input validation	Pattern matching	Unit tests
Tool access	RBAC	Permission audit
Memory encryption	AES-256	Key rotation
Audit logging	Immutable store	Log integrity
Rate limiting	Token bucket	Monitoring

---

Document maintained by CODITECT Security Team