Adam Binks is the CEO of Syrenis, a company that helps large, data-sensitive organizations turn consent and preference management into a commercial advantage. Not a 20-page notice nobody reads, but a real preference architecture that improves retention, engagement, and audit-readiness. Their customers include Fortune 500 brands across financial services, healthcare, insurance, publishing, and automotive, and they recently launched US operations to support enterprise customers across North America.

Privacy technology is often treated as a cost of doing business rather than an investment that supports broader company goals. As a result, securing funding can be challenging for privacy leaders because the C-suite weighs their ask against budget requests from other teams tied more directly to revenue and growth. Overcoming this hurdle requires demonstrating to leadership the value privacy software and other initiatives bring to the organization. So, how can privacy leaders build a strong case for the budget they need?

Winning leadership budget approval requires connecting privacy investments to how they reduce risk and create operational efficiency. Privacy leaders need to work with marketing, finance, sales, and other business teams to understand their priorities and how privacy tech and other initiatives support those functions. This approach helps leadership understand that approving a budget line item for a new consent management platform, for example, not only supports compliance efforts but also enables sales and marketing teams to communicate better with customers and prospects, increasing opt-in rates and bringing more revenue through the sales funnel. By using company-specific examples and ROI metrics, privacy leaders can make a stronger case to the C-suite that privacy benefits the entire organization.

In this episode of She Said Privacy/He Said Security, Jodi and Justin Daniels talk with Adam Binks, CEO of Syrenis, about what it takes to secure privacy budget approval. Adam explains why these requests can stall and how privacy teams can gain leadership's support by aligning their goals with other areas of the business. He shares tips on how privacy pros can pitch software as a marketing and sales enabler and revenue driver, and why it's essential to tie privacy spend to measurable returns on investment to earn funding approval from the C-suite. Adam also discusses how teams can use AI to analyze historical data and strengthen the business case for new technology.

Camilo Artiga-Purcell is General Counsel at Kiteworks, where he leads the company's global legal operations across commercial contracts, data privacy and cybersecurity compliance, M&A, litigation, and regulatory strategy. He has advised on seven acquisitions and brings over a decade of experience litigating complex commercial disputes from inception through trial and appeal. A recognized voice on AI governance and data security risk, Camilo regularly publishes on the legal challenges organizations face as they adopt emerging technologies.

AI agents are becoming more common in everyday business operations, gathering data and carrying out tasks for organizations. Traditional governance policies typically place guardrails around the AI tools human employees can use, what data they can access, and how that information can be used for business purposes. Companies need to extend those same controls to AI agents as they deploy them to reduce risk and meet compliance requirements under a growing patchwork of AI regulations and existing US and global privacy laws. So, what does it take to govern AI agents effectively?

Before allowing AI agents to access and use sensitive data, organizations need to establish governance policies that define what they can or cannot do with it. Companies can then use tools like software wrappers to implement these policies on the front end while generating an audit trail on the back end that logs the agent's identity, the prompt, where it went, and what it did. Those records can plug into e-discovery systems to show whether the agent operated in compliance and provide a clear path to reconstruct its activity if it goes rogue. Alongside these controls, companies should also put security measures in place when they adopt AI models, rather than after problems arise. Employees also need training on how to use AI responsibly, with ongoing education that evolves with the technology.

In this episode of She Said Privacy/He Said Security, Jodi and Justin Daniels talk with Camilo Artiga-Purcell, General Counsel at Kiteworks, about governing AI agents as their use expands across organizations. Camilo explains why governance policies need to be applied directly to AI agents, with evidence-quality audit trails that document their activity. He discusses the importance of proactively embedding security controls into AI tools, highlighting the risks surrounding certain AI models. Camilo also shares his insights into rising AI costs and how organizations can determine which models are appropriate for different teams, and he offers practical tips for verifying AI outputs and pressure testing the results.

Cillian Kieran is the Founder and CEO of Ethyca, where he leads its product vision, engineering strategy, and growth. A serial entrepreneur and privacy engineer with two decades of experience, he launched Ethyca in 2018 to bring privacy-by-design infrastructure to developers. Earlier, he built the digital consultancy CKSK to 100-plus employees across four countries, serving clients like PepsiCo, Heineken, and PlayStation. He pairs deep technical grounding with a track record of scaling data-intensive businesses.

As companies expand their use of AI, they need guardrails around how the technology is used by employees and how those systems use enterprise data. Because models are trained on data, bias can be introduced through this information, while systems also continue to use this data to perform different tasks. This creates risk, and managing it requires evaluating what data a model or tool can access, its intent when it uses that data, and the economic, ethical, and regulatory implications of that intended use. So, what controls do companies need to manage AI and company data safely?

Because AI wants to satisfy a user's request, it will keep trying to access data unless clear boundaries define what it can use. Using AI responsibly requires managing the data behind it alongside the technology itself. To do this effectively, companies need to know what data they have collected, where it came from, whether they have sufficient consent, and what legal basis applies to its use. Once companies understand those elements, they can give AI access to the information it needs for a specific purpose while limiting what falls outside that use, allowing them to leverage the value of their data while minimizing risk. Governance also needs to move from written policies into controls that can be enforced on a system in real time. And while AI can streamline processes like privacy risk assessments by gathering information and comparing it against policies, past assessments, and relevant requirements, human privacy experts still need to review the output for accuracy and challenge AI when it's wrong.

In this episode of She Said Privacy/He Said Security, Jodi and Justin Daniels talk with Cillian Kieran, Founder and CEO of Ethyca, about the importance of unifying AI governance and data governance. Cillian explains how bringing these two areas together helps companies stay in control of their data while allowing the technology to support the business. He discusses why companies are turning to open-source models for greater sovereignty and cautions that bringing a model in-house can still introduce risks. Cillian also addresses the tension between AI's need for data and privacy's data minimization principles, and he stresses why professionals need to think critically, question AI outputs, and avoid relying on AI as a crutch.