The RSAC Innovation Sandbox is putting a spotlight on the pressure points shaping enterprise security in 2026, signaling where buyers and builders will focus next. The program’s curated themes point to a year when artificial intelligence, identity sprawl, and supply chain trust collide with fast-moving attack methods.
This year’s selection centers on agentic AI governance, non-human identity management, social engineering defense, supply chain provenance, and AI-native code security. The emphasis suggests a shift from point fixes to systemic controls that can stand up to automation and scale. It also reflects growing concern that new AI tools can speed both defense and abuse.
The lineup of RSAC’s Innovation Sandbox this year reads like a map of enterprise security’s most urgent pressure points in 2026: agentic AI governance, non-human identity management, social engineering defense, supply chain provenance, and AI-native code security, among others.
Why These Themes Matter Now
For two decades, the Innovation Sandbox has elevated young companies attacking fresh problems. The 2026 topics mirror the risks now moving from labs into daily operations. Security teams face automated attacks, sprawling machine identities, and complex software supply chains. At the same time, developers ship code faster with AI assistance, which can introduce new flaws as well as speed fixes.
Past security cycles were shaped by cloud growth, remote work, and ransomware. Today, AI agents and toolchains are the catalysts. They bring speed and autonomy, but also new failure modes. The Sandbox lineup maps to those fault lines.
Agentic AI Governance Takes Center Stage
Enterprises are piloting AI agents that plan, act, and call tools without direct human oversight. That raises fresh questions on guardrails, audit trails, and incident response. Governance here means more than model safety. It includes policy, permissions, and containment when agents chain tasks together.
Vendors are racing to monitor prompts, outputs, and tool use, while linking those events to identity and data policies. Buyers will look for controls that are simple to prove in audits and easy to roll back when agents misbehave.
Non-Human Identity Becomes the New Perimeter
Workloads, service accounts, APIs, and bots now outnumber people in many environments. Each has keys, tokens, and roles that can be stolen or abused. Traditional identity tools were built for employees and partners. They are often too slow for ephemeral compute and too coarse for fine-grained permissions.
New approaches focus on automated key rotation, just-in-time credentials, least privilege by default, and continuous verification. The goal is to cut blast radius when a token leaks and to make service-to-service trust observable.
Human-Focused Attacks Demand Fresh Defenses
Social engineering is getting sharper with AI-generated voice, video, and text. Attackers blend believable pretexts with stolen data to bypass training and filters. Defenders are turning to stronger verification at critical steps, like payments and account changes, and to systems that check behavior, not just content.
Security leaders also stress playbooks that add friction at high-risk moments. This may include secondary approval for sensitive actions and clearer signals when messages are spoofed.
Proving the Software Supply Chain
Enterprises want to know who built their code, what it contains, and how it changed across builds. Past supply chain incidents exposed weak links in build systems and third-party dependencies. This year’s focus on supply chain provenance suggests a push for signed artifacts, tamper-evident logs, and traceable bills of materials.
Strong provenance can help teams respond faster. If a flaw or compromise appears, they can identify which systems are affected and roll back with confidence.
Securing AI-Native Code and Toolchains
Developers now rely on AI to write and review code. That raises questions about training data, license risk, and pattern reuse of insecure snippets. Tools are emerging that scan AI-suggested code in real time, enforce policy on generated content, and test for logic flaws that static checks might miss.
The build stage is changing too. AI can assemble pipelines, configure infrastructure, and refactor code at scale. Controls will need to verify not only the output, but also the automation that produced it.
What Buyers Should Watch
- Evidence over claims: signed logs, measurable guardrails, and clear rollback paths.
- Fit with existing stacks: identity, CI/CD, and data controls must connect.
- Operations at scale: automation that reduces toil, not new silos.
- Human factors: workflows that add checks without blocking work.
The 2026 Innovation Sandbox signals where security dollars may flow next. AI agents need oversight. Machine identities must be tamed. People require support as social scams grow sharper. Software supply chains must prove trust from code to cloud. And AI-driven development needs safety nets built into the dev and build process.
Expect more products that tie these threads together through identity-aware policies and signed evidence. The test will be how well these tools fit real teams and real budgets. As the year unfolds, watch for proof points from pilots, regulator interest in AI controls, and early case studies that show measurable risk reduction.
Kirstie a technology news reporter at DevX. She reports on emerging technologies and startups waiting to skyrocket.
