Introduction
As AI continues to reshape how enterprises operate, one challenge stands above all others: trust. At Capital One Software, I've led initiatives around Databolt, our tokenization platform, focusing on AI data security and healthcare industry applications. Through this work, I've learned that building enterprise AI systems requires more than just cutting-edge technology. It demands a fundamental rethinking of how we approach security, compliance, and user trust.
The Trust Equation
Trust in AI systems isn't binary; it's a spectrum built on three foundational pillars:
- Security by Design - Protection baked into every layer
- Transparent Governance - Clear accountability and auditability
- User Control - Empowering end-users with meaningful choices
Security by Design
In my experience transitioning from Navy EOD to tech leadership, I've seen parallels between defusing explosive devices and building secure AI systems. Both require:
- Systematic risk assessment - Identify vulnerabilities before they become exploits
- Defense in depth - Multiple layers of protection, never relying on a single safeguard
- Fail-safe mechanisms - Graceful degradation when things go wrong
Key Principles
export async function processAIRequest(
input: string,
userId: string
): Promise<AIResponse> {
if (!validateInput(input)) {
throw new ValidationError("Invalid input detected");
}
const rateLimitOk = await checkRateLimit(userId);
if (!rateLimitOk) {
throw new RateLimitError("Rate limit exceeded");
}
const sanitized = await sanitizeInput(input);
return await processWithMonitoring(sanitized);
}
Compliance First
Healthcare and financial services have strict regulatory requirements: HIPAA, GDPR, SOC 2. Rather than treating compliance as a checkbox exercise, integrate it into your product development lifecycle:
- Data minimization - Collect only what you absolutely need
- Purpose limitation - Use data only for its stated purpose
- Retention policies - Auto-delete data when no longer needed
- Access controls - Role-based permissions with audit trails
Building Trust Through Transparency
Users need to understand:
- What data is being collected
- How it's being used
- Who has access
- How long it's retained
At Capital One Software, we've found that transparency doesn't mean exposing proprietary algorithms. It means clear communication about data handling practices and giving users meaningful control.
Lessons from the Field
1. Start with the Threat Model
Before writing a single line of code, map out:
- What assets are you protecting?
- Who are the adversaries?
- What attack vectors exist?
- What's the blast radius of a breach?
2. Automate Security Testing
Manual security reviews don't scale. Build automated checks into your CI/CD pipeline:
- Static analysis (SAST)
- Dynamic analysis (DAST)
- Dependency scanning
- Secret detection
3. Plan for Incidents
Not "if" but "when." Have an incident response plan that includes:
- Detection and monitoring
- Containment procedures
- Communication protocols
- Post-mortem processes
The Product Manager's Role
As a PM leading AI initiatives, my job isn't to write the security code. It's to:
- Set the standard - Make security a non-negotiable requirement
- Prioritize correctly - Balance speed with safety
- Bridge gaps - Translate between security teams and business stakeholders
- Measure what matters - Track security metrics alongside feature velocity
Looking Ahead
The future of enterprise AI depends on our ability to build systems that are:
- Secure without being locked down
- Innovative without being reckless
- Powerful without being opaque
As I continue leading initiatives at Capital One Software, these principles guide every decision. The goal isn't perfect security (that's impossible), but appropriate security for the risk profile and use case.
Conclusion
Building AI systems that enterprises can trust requires more than technical expertise. It demands a mindset shift: from "move fast and break things" to "move deliberately and build things right." The companies that master this balance will lead the next era of enterprise technology.
About the Author: Harper Foley is a Senior Product Manager at Capital One Software, leading AI data security and healthcare industry use cases for Databolt. Former Navy EOD officer and M&A analyst, now building systems at the intersection of AI, security, and enterprise needs.
