In a mature AWS estate, the humans are a rounding error. The population that actually holds privilege is roles, workloads, service accounts, CI/CD runners, Lambda execution roles, and now AI agents that assume roles and call tools on their own. Most of it was provisioned outside any joiner-mover-leaver process, carries broad or wildcard permissions, and never gets deprovisioned.

Agents make it sharper: a workload chaining sts:AssumeRole or invoking a tool with a scoped-too-wide credential is a privilege decision no human ever reviewed. The table compares how teams are discovering the non-human identities they already have, scoping and expiring that access, and deciding what an autonomous agent is actually allowed to do, without grinding the platform to a halt.

AI is only as good as the data behind it, and right now most organizations aren’t set up to trust that data. Telemetry volumes keep climbing, tools stay fragmented across security and IT, and costs rise faster than budgets can keep up. SOC teams need confidence that their detections map to real threats instead of noise. Operations and platform teams need fast access to the right data to troubleshoot and resolve incidents, without waiting on rehydration or juggling five different tools.

Hear from and discuss with fellow industry leaders about how pairing an intelligent telemetry strategy, one that routes, shapes, and governs data before it hits expensive storage and tools, builds the foundation that teams need to use AI effectively and efficiently. On top of that foundation, teams get faster incident response, stronger governance, and a real path toward agentic operations where AI can act on trustworthy, accessible data.

The pace of AI development has made technical teams faster and more capable than ever. What was cutting-edge a year ago is quickly becoming table stakes.

The harder question is whether the data and architecture behind those AI initiatives are evolving just as quickly. Models continue to improve, but their ability to deliver meaningful business outcomes still depends on something much more fundamental: trusted, real-time enterprise context.

Most organizations are beginning to realize the same thing. AI is only as powerful as the information it can access. As AI becomes embedded across the business, decisions around data architecture, real-time access, and infrastructure are becoming just as important as the models themselves. The choices organizations make today will determine how effectively AI scales, how quickly it adapts to change, and ultimately, the value it delivers to the business.

SingleStore is bringing together a select group of senior technology leaders to exchange perspectives on how they’re building the foundations for enterprise intelligence.

What we’ll discuss:

• Why real-time enterprise context has become the foundation for AI that organizations can trust and scale.
• The architectural decisions that enable intelligent applications to grow without adding unnecessary complexity.
• How leading organizations are modernizing fragmented data environments to support faster, more informed decision-making.
• Strategies for improving AI efficiency while balancing performance, governance, and cost.

Join us for a focused conversation with peers who are working through the same problems, at different stages, with different constraints, and with a lot of insights to share.

Most FedRAMP 20x planning has focused on January 2027. But three requirements come due before the end of this year, and two of them apply to Rev5 holders who never opted into 20x at all:

• Machine-readable packages
• The Rev5 Ready conversion window
• New vulnerability reporting requirements

This is a small, invitation-only discussion for the executives accountable for getting their programs through it. You’ll be at the table with the RISCPoint assessors and advisors running these conversions for clients today, alongside a peer group facing the same year-end decisions.

You’ll leave knowing exactly what the next four months require, and what to look for in the partner who gets you there.

Healthcare data has outgrown the systems built to manage it. Records, imaging, claims, research, and operational data now sprawl across clouds, vendors, and legacy platforms faster than most health systems can govern them. The result is a quiet, widening gap: most organizations can restore their systems after an incident, but far fewer can prove their data is clean, governed, and recoverable when it matters most.

That gap is no longer an IT problem. It shapes cyber resilience, regulatory exposure, and whether a health system is genuinely ready to put AI to work, because AI readiness is not a question of data volume. It is a question of data trust, and the gap is often wider than the board believes.

This is a closed-door, peer-level conversation for healthcare executives working through that reality. Over an evening, leaders examine the three pillars that increasingly define data strategy, data foundation, governance, and cyber recovery, and the harder question of how to sequence them under real constraints.

The discussion is candid and confidential: no slides, no recording, no attribution. Perspectives from AWS and RapidScale ground the conversation in what is working in the market today, but the value sits with the room.

Most enterprise security stacks were built to inspect packets, flows, files and sessions. The material that now leaves the business does not look like any of those. It is a paragraph of contract language, a customer list, a set of forecast numbers or a block of source code, pasted into a browser window and sent as encrypted traffic to a domain that looks entirely legitimate.

Ask most organizations how many AI tools their staff use and the honest answer is an estimate. Ask what left the business in a prompt last week and there is usually no answer at all. The tooling that would catch the same data leaving as an email attachment does not see it.

This roundtable brings together security and infrastructure leaders from across UK enterprise to compare what they can actually see, what they have chosen to block, and where blocking has cost more than it protected.

The discussion covers the visibility gap between sanctioned and unsanctioned AI use, who holds the authority to say no when a business unit has already bought the tool, and what a workable policy looks like when the alternative is people using personal accounts on personal devices.

Healthcare organizations don’t lack an AI strategy. They lack a foundation that can carry one. Claims processing, prior authorization, revenue cycle, and payer-provider connectivity all run on legacy logic that’s too opaque to automate and too critical to gamble on with a rip-and-replace project.

This executive roundtable, hosted by SoftServe and AWS, is about closing that gap, moving healthcare organizations from legacy operations to AI-ready ones through modernization designed specifically to enable AI, not just precede it. Using deterministic system mapping, architecture and code analysis, and a phased transformation approach powered by SAAM, AWS Transform, Kiro, and SoftServe’s Agents Management Platform, we will show how organizations can build the visibility and confidence AI enablement depends on.

Modernization is the foundation. AI is the outcome. This is where healthcare leaders see how the two connect and how to move on both without betting the business on a rewrite.

IT, Digital, Data, AI, and Engineering leaders — VP and Director+ — from payers, providers, and health tech companies within the healthcare industry.