J. Paul Haynes, CEO of Cinchy – Interview Series

J. Paul Haynes, CEO of Cinchy, is a cybersecurity and technology executive with decades of experience building and scaling enterprise security companies. He is best known for his leadership at eSentire, where he helped grow the company into a major Managed Detection and Response provider, and brings a strong background in engineering, cybersecurity, private equity-backed growth, and enterprise risk to Cinchy as the company focuses on trusted AI adoption and governance.

Cinchy is an enterprise AI governance and data collaboration company focused on helping organizations securely connect AI, systems, and data. Its PeriMind platform acts as a control plane for AI actions, giving enterprises visibility, policy enforcement, security, and auditability over how AI agents interact with business systems, while its Data Collaboration Platform helps teams manage and share governed data across the organization without relying on complex integrations or duplicative data silos.

You spent more than 15 years helping build and scale eSentire into one of the cybersecurity industry’s most recognized companies. What lessons from that journey are you bringing into your new role as CEO of Cinchy, and why did you believe now was the right time to focus on AI governance and trusted AI adoption?

One of the biggest lessons I learned at eSentire is that organizations don’t struggle because they lack technology. They struggle because emerging technologies create operational complexity faster than most companies can manage on their own. I see AI following a remarkably similar path.

Today’s challenge isn’t getting employees to experiment with AI. Most organizations are already doing that. The real challenge is moving AI into production, where decisions affect customers, financial outcomes, regulatory compliance, and business operations.

Highly regulated industries like financial services illustrate this perfectly. They aren’t moving slowly because they don’t understand AI. They’re moving cautiously because they can’t yet trust AI with mission-critical work. Before an AI system can make decisions, organizations need answers to fundamental questions: who approved those decisions? How are they monitored? When does a human intervene? What happens if the model drifts, fails, or becomes unavailable?

Those questions remind me of where cybersecurity was fifteen years ago. Security evolved from a technology problem into an operational discipline. I believe AI governance is on the same trajectory.

That’s why I joined Cinchy. We believe trusted AI adoption (not AI experimentation) will define the next decade, and we see managed AI governance becoming just as important as managed cybersecurity became.

Many enterprises have experimented with generative AI over the past two years, yet relatively few have successfully deployed AI across mission-critical workflows. What are the biggest barriers preventing organizations from moving from pilots to production?

I don’t think companies are struggling because the technology isn’t good enough. I think they’re struggling because the stakes get a lot higher once AI moves beyond experimentation.

A pilot is one thing. Putting AI into production is something else entirely. Now it’s accessing enterprise data, interacting with business systems, maybe even making decisions on behalf of employees. That’s when leadership quite rightly starts asking, “How do we control this? How do we monitor it? And how do we know when we need a person involved?”

Those aren’t reasons to slow down AI adoption. They’re the things that make AI adoption possible in the first place. If you can answer those questions with confidence, organizations move much faster.

You have spent much of your career securing enterprise environments against cyber threats. How does securing an AI-powered organization differ from securing a traditional IT environment?

Traditional cybersecurity focuses on protecting endpoints, identities, networks and cloud environments. AI adds another permanent enterprise threat surface. Every AI system introduces new risks. Model access, prompt injection, excessive permissions, model drift, autonomous decision-making, and entirely new attack paths that simply didn’t exist before.

The important point is that AI security isn’t separate from cybersecurity. It extends cybersecurity. Organizations still need every existing security control, but now they also need security designed specifically for AI systems operating in production.

Shadow IT has long been a concern for security teams, but we are now seeing the rise of shadow AI. How significant is this challenge, and what risks are organizations underestimating?

Shadow AI is becoming one of the fastest-growing business risks because executive teams are encouraging rapid AI adoption without always putting governance in place first. Most employees aren’t acting maliciously. They’re trying to be more productive. The problem is that AI systems inherit whatever permissions users already have.

An executive experimenting with a public AI model could unintentionally expose confidential financial data, customer information, or intellectual property simply by asking AI to analyze it.

That’s why organizations need visibility, policy controls, and governance, not to slow AI adoption down, but to make it safe enough to scale.

AI agents are increasingly being given access to enterprise systems, data, and decision-making processes. What new accountability and governance challenges emerge when AI systems can act autonomously rather than simply generate content?

AI agents fundamentally change the governance conversation because they’re no longer generating content. They’re taking actions. Whenever an autonomous system can approve transactions, access enterprise systems or make business decisions, organizations need complete accountability.

Every action should answer simple questions:

• What happened?
• Why did it happen?
• Which AI agent made the decision?
• Who authorized it?
• When should a human intervene?

Those principles aren’t new. Industries have lived with accountability requirements for decades under regulations like Sarbanes-Oxley. AI simply extends those same expectations to autonomous digital workers.

Many organizations are racing to deploy AI because of competitive pressure. How can business leaders balance the need for speed with the need for governance, compliance, and risk management?

Governance shouldn’t slow AI adoption. It should accelerate it. Organizations move faster when leadership trusts the environment they’re building. That means establishing a secure, monitorable AI architecture, continuously evaluating models for drift and performance, managing access to enterprise systems, and having clear operational controls before AI reaches production.

Just as organizations adopted managed detection and response to simplify cybersecurity, I believe many will adopt managed AI governance to gain expertise without having to build large specialist teams internally.

Boards of directors are becoming more involved in AI oversight. What questions should board members be asking management teams about AI strategy, governance, and accountability?

Boards should ask one fundamental question: can management demonstrate that AI is operating under the same level of governance as every other critical business system?

That means understanding where AI is being used, who owns it, how decisions are monitored, how risk is managed, and how the organization would continue operating if an AI service became unavailable. The organizations that succeed won’t necessarily be the ones adopting AI fastest. They’ll be the ones building the operational discipline to trust AI at scale.

The cybersecurity industry has spent decades developing frameworks, controls, and best practices. Are there lessons from cybersecurity that can be directly applied to AI governance, or does AI require an entirely new approach?

Cybersecurity offers an excellent blueprint for AI governance. Identity management, least privilege, continuous monitoring, audit trails, incident response, and resilience all remain essential. What’s new is that AI introduces challenges traditional systems never had, including model drift, hallucinations, prompt manipulation, and dependence on third-party foundation models. So, AI doesn’t replace cybersecurity. It builds on it with an additional governance layer.

What does “trusted AI adoption” actually look like in practice? Are there specific governance principles or operational safeguards that every enterprise should have in place before scaling AI initiatives?

I tend to think about it the same way we thought about cybersecurity years ago. Nobody asked whether security created value. The question was whether you trusted your environment enough to keep the business running.

AI is heading in exactly the same direction. If you can’t see what it’s doing, control what it has access to, or understand why it’s making certain decisions, you’re probably not going to let it handle anything important.

Once you have that confidence, adoption happens naturally. Organizations stop treating AI like an interesting experiment and start treating it like another part of the business.

Looking ahead three to five years, how do you see the relationship between AI governance and competitive advantage evolving? Will strong governance become a differentiator, or simply a requirement for doing business?

Over the next three to five years, strong AI governance won’t be a competitive differentiator. It will become a business requirement. Organizations that establish governance early will adopt AI faster because they’ll have the confidence to move critical workloads into production. Those that don’t will remain stuck in experimentation while competitors realize measurable gains in productivity, innovation, and customer experience. Ultimately, governance won’t slow AI adoption. It will be the reason organizations can trust AI enough to fully embrace it.

Thank you for the great interview, readers who wish to learn more should visit Cinchy.