Security Mindset for AI Developers

Building secure AI applications requires a different way of thinking. Here are the core principles that should guide every decision.

The Four Security Principles

1. Zero Trust

Never trust any input, even from authenticated users.

# Wrong: Trusting user input
def process_request(user, query):
    if user.is_authenticated:
        return llm.generate(query)  # User could still inject

# Right: Validate regardless of authentication
def process_request(user, query):
    if user.is_authenticated:
        validated_query = validate_and_sanitize(query)
        return llm.generate(validated_query)

2. Defense in Depth

Layer multiple security controls. If one fails, others protect you.

# Multiple layers of defense
def secure_chat(user_input):
    # Layer 1: Input validation
    if not validate_input(user_input):
        return "Invalid input"

    # Layer 2: Content filtering
    filtered_input = filter_dangerous_patterns(user_input)

    # Layer 3: Guardrails
    response = guardrails.process(filtered_input)

    # Layer 4: Output validation
    safe_response = sanitize_output(response)

    # Layer 5: Logging for monitoring
    log_interaction(user_input, safe_response)

    return safe_response

3. Principle of Least Privilege

Give the LLM only the permissions it needs.

Bad Practice	Good Practice
Full database access	Read-only to specific tables
All file operations	Read from whitelisted paths
Unrestricted API calls	Rate-limited, scoped tokens
Admin email access	Send-only, templated messages

4. Assume Breach

Design as if the LLM will be compromised.

# Assume breach: Limit blast radius
class SecureAgent:
    def __init__(self):
        # Separate credentials per capability
        self.read_db = DatabaseConnection(role="reader")
        self.write_db = DatabaseConnection(role="writer")

        # Audit everything
        self.audit_log = AuditLogger()

        # Automatic timeouts
        self.max_operation_time = 30  # seconds

        # Sandboxed execution
        self.sandbox = Sandbox(
            network=False,
            filesystem="read_only",
            max_memory="512MB"
        )

Security Checklist for Every Feature

Before deploying any AI feature, ask:

1. What's the worst case? If this is exploited, what happens?
2. Who controls the input? Users, documents, or external systems?
3. What can the LLM do? Tools, data access, actions?
4. How will we detect abuse? Logging, monitoring, alerts?
5. Can we limit damage? Rate limits, permissions, rollback?

The Security Developer's Mantra

"The LLM will do exactly what it's told. Make sure only you're telling it."

Every input to the LLM is an instruction. User messages, documents, tool outputs - they all influence behavior. Your job is to ensure only intended instructions reach the model.

Key Takeaway: Security isn't a feature you add later. It's a mindset you apply from the first line of code. :::