AI's security paradox: how enterprises can have their cake and eat it too

Sponsored Post Here's the contradiction grinding on enterprise IT leaders like you: AI's value lives in your unstructured content (the sprawling information corpus that actually runs the business). You want to unlock it, but it's risky; one slip-up in permissions and you could find yourself explaining an information breach to the ICO.

Do you want speed and innovation, or strong security? It's a gnarly problem. ere's a spoiler: you can have both. The answer lies in designing AI with governance baked in from the beginning, so that you can move quickly while maintaining control.

Baking good governance into your information management carries different implications in UK and European markets. GDPR is already table stakes there, and EU and UK AI regulations are advancing fast. Meanwhile, shadow AI is spreading as teams chase productivity wins outside sanctioned channels, creating unmapped compliance blind spots. With tight budgets, tangled infrastructure, and scarce AI talent, what you need is an approach that works with what you've got now, not a fantasy green-field deployment.

Building governance in, not bolting it on

A governance-first approach provides the blueprint. Controls from day one should include centralized policies, least-privilege access for data and models, model access controls, auditable histories, and human oversight for high-risk operations. This isn't theoretical; it's doable today.

LLM privacy should be the default setting. Your content must never train external models, and every output must trace back to its source. Every AI action, from prompts to data retrievals, tool invocations, and outputs, should be immutably logged, creating audit trails that satisfy regulators and boards alike.

Start by layering secure AI onto existing systems such as productivity suites, case management tools, and CRM interfaces. Technology integrations that honor current permissions, taxonomies, and labels prevent shadow AI through centralized policies and permission-aware data access .

Use no-code and low-code interfaces to bridge skills gaps. Maintain model flexibility to dodge vendor lock-in, enabling teams to switch models on a per-task basis under consistent governance. When legacy systems can't support permission-aware access or auditable AI activity, a proper content layer becomes your responsible path forward.

Defence-in-depth isn't optional. Classification, DLP, encryption, identity management, and tuned threat detection must all protect your content. AI activity monitoring provides real-time visibility into retrievals and outputs, with evaluation against toxicity and hallucination thresholds. Ground AI in governed, specified content to keep answers tied to verifiable sources. This will help to eliminate hallucinations and prevent oversharing.

From pilots to production

For optimal business gain, measure and instrument the path from pilot to business impact. Start with narrow high-value workflows. Test and red-team agents before scaling, tracking accuracy, latency, and exceptions. Then progress from assistive use cases to orchestrated workflows, and finally to semi-autonomous operations where risk profiles allow.

Done properly, the outcomes become tangible quickly. You'll benefit from fewer information leaks and faster incident response. You'll automate your document classification and extraction, cutting down backlogs. Consolidation will deliver a predictable TCO. Above all, transparent auditability will build regulator and stakeholder trust. That's the currency that matters when regulations shift.

You don't have to choose between innovation and security, but you do have to architect for both. That means designing in governance, privacy, and accountability from day one.

Want to find out more, with practical guidance on implementing governance and compliance in the AI era? Box Summit London runs 21st October. Register here.

Sponsored by Box.