Security Best Practices

Security Best Practices for AI Agents

Security is critical when deploying AI agents to production. This guide covers essential practices to keep your agents, data, and users safe.

The Security Mindset

Principle: Assume breach. Design systems that remain secure even if one component is compromised.

Key Principles:

Least Privilege - Minimum permissions needed
Defense in Depth - Multiple security layers
Zero Trust - Verify everything, trust nothing
Fail Secure - Errors should deny access, not grant it

Authentication & Authorization

API Keys

Do’s ✅

Create org API keys in Settings → API Keys (sk_live_...)
Store third-party tokens only in Integrations connection setup (encrypted)
Use environment variables in your own apps that call the Auteryn API — never commit keys to git

Don’ts ❌

OAuth Tokens

Best Practices:

Use OAuth over API keys when available
Request minimum scopes needed
Refresh tokens before expiry
Revoke unused tokens

User Authentication

For user-facing agents:

# Verify user identity
if not user.is_authenticated():
    return "Please log in first"

# Check permissions
if not user.has_permission("view_data"):
    return "Access denied"

Data Protection

Sensitive Data Handling

PII (Personally Identifiable Information):

# ❌ Bad: Logging PII
logger.info(f"User email: {user.email}")

# ✅ Good: Redact PII
logger.info(f"User: {user.id}")

# ✅ Good: Hash PII
import hashlib
hashed = hashlib.sha256(user.email.encode()).hexdigest()
logger.info(f"User hash: {hashed[:8]}")

Credit Card Data:

Data Encryption

At Rest:

# Auteryn encrypts all data automatically
# But for extra sensitive data:
from cryptography.fernet import Fernet

key = agent.get_secret("ENCRYPTION_KEY")
cipher = Fernet(key)

# Encrypt
encrypted = cipher.encrypt(b"sensitive data")

# Decrypt
decrypted = cipher.decrypt(encrypted)

In Transit:

Always use HTTPS for APIs
Verify SSL certificates
Use TLS 1.3 minimum

Input Validation

Sanitize User Input

SQL Injection Prevention:

# ❌ Bad: String concatenation
query = f"SELECT * FROM users WHERE email = '{user_input}'"

# ✅ Good: Parameterized queries
query = "SELECT * FROM users WHERE email = %s"
cursor.execute(query, (user_input,))

XSS Prevention:

# ❌ Bad: Raw HTML
html = f"<div>{user_input}</div>"

# ✅ Good: Escape HTML
from html import escape
html = f"<div>{escape(user_input)}</div>"

Command Injection Prevention:

# ❌ Bad: Shell injection risk
os.system(f"ls {user_input}")

# ✅ Good: Use subprocess with list
import subprocess
subprocess.run(["ls", user_input])

Validate All Inputs

def validate_email(email):
    import re
    pattern = r'^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$'
    if not re.match(pattern, email):
        raise ValueError("Invalid email")
    return email

# Use validation
try:
    email = validate_email(user_input)
except ValueError:
    return "Please provide a valid email"

Access Control

Role-Based Access Control (RBAC)

Define roles and permissions:

roles = {
    "admin": ["read", "write", "delete", "manage_users"],
    "editor": ["read", "write"],
    "viewer": ["read"]
}

# Check permission
if "write" not in user.permissions:
    return "Access denied"

Principle of least privilege

Avoid: Granting write/admin scopes on GitHub, Google, or Slack when the agent only reads data.

Prefer: Read-only connection scopes in Integrations, then add write scopes only when the agent must create or update resources.

Credentials

Store integration tokens in Integrations → [connection] setup — they are encrypted at rest. Never paste API keys into agent instructions or commit them to git.

Audit Logging

What to Log

Do log:

Authentication attempts
Permission checks
Data access
Configuration changes
Integration calls
Errors and exceptions

Don’t log:

Passwords or API keys
Credit card numbers
PII (unless necessary and encrypted)
Full request/response bodies

Log Format

import logging

logger.info({
    "event": "data_access",
    "user_id": user.id,
    "resource": "customer_data",
    "action": "read",
    "timestamp": "2026-04-02T10:15:23Z",
    "ip_address": request.ip,
    "result": "success"
})

Compliance

User Rights:

Right to access data
Right to deletion
Right to portability
Right to rectification

Implementation:

# Data export
data = agent.export_user_data(user_id)

# Data deletion
agent.delete_user_data(user_id)

# Anonymization
agent.anonymize_user_data(user_id)

Data Retention

Set retention policies:

agent.configure_retention(
    logs="30d",
    snapshots="90d",
    conversations="1y",
    analytics="2y"
)

Incident Response

Response Plan

Detect - Monitoring alerts on issue
Contain - Isolate affected systems
Investigate - Determine root cause
Remediate - Fix the issue
Recover - Restore normal operations
Review - Post-mortem and improvements

Emergency Contacts

Security issues: security@auteryn.ai
Critical bugs: support@auteryn.ai
24/7 hotline: +1-555-AGENT-911 (Enterprise)

Security Checklist

Before Deployment

Ongoing

Resources

Questions?

Is Auteryn SOC 2 compliant? Contact security@auteryn.ai for current compliance status and enterprise requirements.
Where is data stored? US or EU regions (you choose).
Can I use my own encryption keys? Yes (Enterprise plan).
Do you support HIPAA? Yes, with BAA (Enterprise plan).
How do you handle security incidents? 24/7 monitoring, immediate response, transparent communication.

View all FAQs →