How to Build an AI Security Program: A CTO's Guide

Every CTO deploying AI features faces the same question: how do we move fast without compromising security? In our conversations with engineering leaders at companies ranging from Series A startups to Fortune 500 enterprises, we've observed a common pattern. Teams ship AI features quickly, security becomes an afterthought, and then a breach or near-miss forces a reactive scramble.

This guide provides a proactive alternative. It's a practical framework for building an AI security program that scales with your organization. It draws on recommendations from the NIST AI Risk Management Framework (AI RMF), which provides a structured approach to identifying and managing AI-specific risks throughout the system lifecycle.

Why Traditional Security Frameworks Fall Short

Your existing application security program likely covers the OWASP Top 10, input validation, authentication, and encryption. These remain essential, but AI systems introduce fundamentally different risks.

Traditional security assumes predictable software behavior. You write code, test it, and deploy it knowing exactly what it will do. LLMs don't work that way. They're probabilistic systems that can produce unexpected outputs, follow injected instructions, or leak sensitive information in ways that static code analysis and penetration testing won't catch.

The OWASP Top 10 for LLM Applications identifies threats specific to AI systems:

• Prompt injection attacks that manipulate model behavior
• Data leakage through model outputs
• Excessive agency in AI agents with tool access
• Training data poisoning
• Supply chain vulnerabilities in model dependencies

Gartner has noted that most enterprises lack dedicated AI security strategies, even as AI adoption accelerates across business functions. The MITRE ATLAS framework further catalogs over 60 techniques specific to adversarial attacks on AI systems, underscoring how different this threat landscape is from traditional application security. Your security program needs to evolve to address these.

Step 1: Conduct an AI Risk Assessment

Before implementing controls, understand what you're protecting and from whom.

Map Your AI Attack Surface

Start by inventorying every AI touchpoint in your organization:

Most organizations underestimate their AI footprint. We've worked with companies that discovered 3x more AI integrations than leadership knew about when conducting thorough assessments.

Classify Data Exposure

For each AI system, document what data it can access:

• Direct inputs: What information do users or systems send to the model?
• Context and RAG sources: What documents, databases, or APIs does the AI query?
• Output destinations: Where do AI responses go, and who sees them?

This classification drives your control requirements. An internal summarization tool with access to all company documents needs different protections than a customer FAQ chatbot.

Identify Threat Actors and Scenarios

Consider both external and internal threats:

External threats:

• Attackers using prompt injection to exfiltrate data
• Competitors extracting proprietary information through chat interfaces
• Bad actors generating harmful content that damages your brand

Internal threats:

• Employees accidentally exposing PII through AI tools
• Shadow AI usage bypassing approved channels
• Developers shipping AI features without security review

For each scenario, estimate likelihood and impact. This prioritizes where to focus.

Step 2: Establish AI Security Governance

Technical controls matter, but governance determines whether they get implemented and maintained.

Define Ownership

Someone needs to own AI security. In smaller organizations, this might be the CTO or VP of Engineering. In larger ones, consider a dedicated AI Security Lead reporting to the CISO.

Clear ownership prevents the common failure mode where security and engineering each assume the other is handling AI risks. The NIST AI RMF emphasizes that effective AI risk management requires clearly defined roles and responsibilities, with accountability structures documented at the organizational level.

Create an AI Security Policy

Document your organization's requirements for AI systems. Key elements include:

Acceptable use:

• Which AI providers are approved for business use?
• What data can and cannot be sent to external AI services?
• What disclosure is required when AI generates customer-facing content?

Security requirements:

• What scanning and monitoring must AI systems implement?
• What approval process applies to new AI features?
• What incident response procedures apply to AI security events?

Compliance mapping:

• How do AI uses align with GDPR, CCPA, HIPAA, or industry regulations?
• What documentation is required for AI systems handling sensitive data?

Implement Review Processes

New AI features should require security review before deployment. Create a lightweight checklist that engineering teams can self-assess:

1. What data does this AI feature access?
2. What could go wrong if the AI is manipulated?
3. How is user input validated before reaching the model?
4. How is model output validated before reaching users?
5. What logging and monitoring is in place?

For higher-risk features, escalate to formal security review.

Step 3: Deploy Technical Controls

With governance established, implement the technical controls that enforce your policies.

Input Protection

Every AI system needs input scanning to detect attacks before they reach the model:

import Wardstone from "wardstone";
 
const wardstone = new Wardstone();
 
async function processUserInput(input: string) {
  // Scan input before processing
  const result = await wardstone.guard(input);
 
  if (result.flagged) {
    // Log the attempt for analysis
    logger.warn("Blocked AI input", {
      category: result.primary_category,
      risk: result.risk_bands,
    });
 
    return { error: "Your request could not be processed." };
  }
 
  // Safe to proceed
  return await llm.complete(input);
}

This catches prompt injection, jailbreak attempts, and other attacks at the perimeter.

Output Protection

Input scanning alone isn't sufficient. Model outputs can contain sensitive data even from benign inputs:

• PII from training data or RAG sources
• Internal system information leaked through verbose error handling
• Harmful content generated despite input filtering

Scan outputs before returning them to users or downstream systems. This is especially critical for AI features that access sensitive data stores.

Privilege Separation for AI Agents

AI agents that can take actions (send emails, query databases, modify records) require strict privilege controls:

• Implement least privilege: agents should only access what they need
• Require human approval for high-impact actions
• Log all agent actions for audit
• Set rate limits to contain potential damage

An agent that can send unlimited emails from your domain is a breach waiting to happen.

Monitoring and Alerting

You can't secure what you can't see. Implement:

• Request logging: Record all AI interactions for forensic analysis
• Anomaly detection: Alert on unusual patterns (volume spikes, repeated blocked attempts)
• Output monitoring: Sample and review AI outputs for quality and safety
• Performance tracking: Monitor latency and error rates to detect availability attacks

Build dashboards that give security and engineering teams visibility into AI system health.

Step 4: Train Your Teams

Technology alone isn't enough. The people building and using AI systems need to understand the risks.

Developer Training

Engineers building AI features should understand:

• Common AI attack vectors and how to defend against them
• Secure coding practices for LLM integrations
• How to use your organization's AI security tools
• When and how to escalate security concerns

Consider incorporating AI security into your existing secure development lifecycle training.

User Awareness

Employees using AI tools should know:

• What data is appropriate to share with AI systems
• How to recognize when AI output seems wrong or manipulated
• Company policies on AI use
• How to report concerns about AI behavior

This is especially important as AI assistants become embedded in productivity tools.

Incident Response Training

Security teams need AI-specific incident response capabilities:

• How to investigate AI security incidents
• What forensic data is available from AI systems
• How to contain AI-related breaches
• When to involve legal or communications teams

Run tabletop exercises with AI-specific scenarios.

Step 5: Measure and Iterate

An AI security program isn't a one-time project. It's an ongoing practice that evolves with your AI usage.

Key Metrics

Track metrics that indicate program health:

Regular Reviews

Schedule quarterly reviews to assess:

• New AI deployments and their security posture
• Emerging threats and attack techniques
• Policy effectiveness and needed updates
• Tool performance and potential improvements

Stay Current

AI security is a rapidly evolving field. New attack techniques emerge regularly, and defenses need to keep pace:

• Follow AI security research and vulnerability disclosures
• Participate in industry forums and working groups
• Update your threat models as your AI usage evolves
• Test your defenses regularly with red team exercises

Common Pitfalls to Avoid

In our experience working with organizations building AI security programs, several mistakes recur:

Starting too late: Don't wait for an incident. Implementing security retroactively is harder and more expensive than building it in from the start. According to IBM's Cost of a Data Breach Report, the average cost of a data breach reached $4.88 million in 2024, and breaches involving AI-related systems or shadow AI usage add additional complexity to containment and remediation.

Over-engineering: A simple, consistently applied security layer beats a complex system that gets bypassed because it's too cumbersome.

Ignoring shadow AI: Employees will use ChatGPT and other tools whether you approve them or not. Create secure, sanctioned alternatives.

Forgetting the output side: Input scanning is necessary but not sufficient. Outputs can leak data even from benign inputs.

Treating it as one-time: AI security requires ongoing attention, not just initial implementation.

Getting Started

You don't need to implement everything at once. A phased approach works well:

Phase 1 (Weeks 1-2):

• Conduct AI inventory and risk assessment
• Deploy input/output scanning on highest-risk systems
• Establish basic AI security policy

Phase 2 (Weeks 3-4):

• Extend protection to remaining AI systems
• Implement monitoring and alerting
• Begin developer training

Phase 3 (Ongoing):

• Establish governance review processes
• Build incident response capabilities
• Continuous measurement and improvement

Next Steps

Building an effective AI security program requires the right combination of governance, technology, and training. The organizations that get this right will be able to deploy AI confidently, capturing competitive advantages while managing risks.

Ready to see how AI security scanning works in practice? Try the Wardstone playground to test detection against real attacks. For enterprise deployments, contact our team to discuss your specific requirements.