How to Build and Implement AI Systems That Businesses Can Trust

Myles McNamara is Tarkenton's lead technical architect and full-stack developer, specializing in building secure, scalable software solutions. He oversees infrastructure, code, and system design, serving as the team's in-house expert. Previously, he worked with Fortune 500 government contractors and ran his own software and hosting companies. In this episode… Integrating responsible AI tools and systems into business operations depends less on the model itself and more on the privacy and security controls a company embeds around it. "We need AI" is often where the conversation starts, but turning that need into a safe and controlled environment requires a clear understanding of where data lives, who can access it, and what the AI system is allowed to do. Those considerations shape whether AI can support the business without creating unnecessary risks. How can organizations design and implement AI in a way that is secure and grounded in real business needs?  Before companies implement a new AI system, they need to set guardrails around how it will operate in practice. Governance needs to be built into the system from the beginning, not left in a policy or bolted on later. Establishing clear data boundaries, access controls, and system-level permissions defines what the AI tool can access and which actions it can perform. Logging and audit trails give companies visibility into how the system is functioning, so if something goes wrong, they can understand what happened and why. AI will continue to evolve, and companies also need to ensure that their privacy and security controls keep pace through regular monitoring and continued improvements.  In this episode of She Said Privacy/He Said Security, Jodi and Justin Daniels talk with Myles McNamara, Principal Software Engineer at Tarkenton, about designing and implementing AI tools and systems responsibly. Myles shares what it takes to build AI agents and systems in a secure, controlled way, including how companies should think about whether AI is needed and how much autonomy it should have. He emphasizes the importance of integrating governance into system design and offers advice for safeguarding data. Myles also shares how engineering teams can balance business expectations with privacy and security concerns and discusses why AI governance might get overlooked in practice.