Enkrypt AI Named Most Innovative Startup at AWS re:Invent 2025, Leading the Future of AI Agent Security
I had one of those “frame the screenshot” moments at AWS re:Invent 2025.
Sitting in the keynote, I watched Matt Garman, CEO of AWS, call out Enkrypt AI on the main stage as one of the “Most Innovative Startups” shaping the future of AI.
Not just “promising.”
Not just “interesting.”
Most Innovative.
That hit home in a way I didn’t expect.
For years, our team has been building around a problem that barely had language: how do enterprises secure AI agents—not just LLMs or models—in real production environments? Long before terms like agent security, agent governance, or MCP security entered the vocabulary, we were mapping attack surfaces and risks for organizations still trying to get their first copilots online.
At AWS re:Invent this year, it felt like the market finally said, “Yes—we see the same future.”
Why this moment mattered
When you’re building ahead of the curve, there’s a lot of silence. The constant but quiet grind mixed with not a lot of external validation. You talk to early customers, you ship features, you run experiments, you throw away more ideas than you keep.
So hearing AWS recognize Enkrypt AI from the keynote stage wasn’t just a “Hey! Look it’s us!” It was a signal that:
For our team, this means a lot. For our customers and partners, it’s further validation that AI governance, and agent oversight are essential for scaling real-world AI.
The Conversations Behind the Scenes
As always, the real insights came not from the stage but from the hallways, booths, and late-night sessions.
And with everyone, one theme kept surfacing:
You can’t safely scale AI if you can’t see, understand, and govern what your AI agents are doing.
MCP, Agentic AI, and What We’re Focused on Now
This year at AWS re:Invent, Merritt Baer and I spent a lot of time with folks who are either just starting with agents or already running them in production.
We walked through real-world patterns for:
This is exactly the problem space Enkrypt AI is built for:
Helping enterprises move fast with agents, without flying blind on risk.
And Most Importantly, Thank You
To everyone who stopped by, challenged our thinking, shared feedback, or explored partnerships in Vegas -Thank You.
To the AWS team and ecosystem, thank you for putting a spotlight on a problem we believe will define and pilot the next decade of AI.
And to the Enkrypt AI team: I’m profoundly proud of what we’ve built, the standards you hold, and the category you are helping define, while showing up each and everyday excited to innovate.
If this is where we are now, I can’t wait for what’s coming next.
Onward. 🚀
Frequently Asked Questions
AI agent security is the practice of securing autonomous AI agents in production environments through real-time policy-based guardrails and governance. It prevents agents from executing unsafe actions, leaking data, or being manipulated by attackers.
- Agents operate independently and can access tools, data, and APIs without human review between decisions.
- Without security controls, agents can be exploited for prompt injection, data exfiltration, and unauthorized tool misuse.
- Enterprise AI governance requires visibility, control, and compliance across all agent deployments and MCP infrastructure.
Secure AI agents in production using runtime agent guardrails that block hallucinations, data leakage, and unsafe actions with ultra-low latency. Policy-based enforcement happens at decision time, not after deployment.
- Real-time guardrails intercept agent outputs before execution, preventing harmful actions instantly.
- Centralized policy engines apply consistent security rules across all agents without manual intervention per request.
- Latency-optimized architecture keeps agent response times under milliseconds while enforcing compliance.
Agent security addresses autonomous decision-making and tool execution, while LLM security focuses on model output safety. Agents introduce new attack surfaces: tool misuse, multi-step reasoning exploitation, and context poisoning across MCP servers.
- Agents call external tools, APIs, and databases—expanding risk beyond text generation to data access and system actions.
- LLM security stops at output filtering; agent security must govern what actions agents are permitted to take.
- MCP infrastructure adds complexity: agents chain multiple model context protocol servers, each a potential attack vector.
Enkrypt AI is recognized as a Gartner Cool Vendor in AI Security 2025 and was named Most Innovative Startup at AWS re:Invent 2025 for agent and MCP security. The platform covers red teaming across 300+ risk categories, runtime guardrails, and centralized policy enforcement.
- Enkrypt AI reduces manual compliance effort by up to 90% through automated governance and policy management.
- Covers the full agent lifecycle: discovery, red-teaming, runtime protection, and compliance auditing.
- Purpose-built for enterprises scaling agents and MCP toolchains without flying blind on risk.
Ready to see how Enkrypt AI governs agents in production without slowing you down? Book a demo to walk through your specific agent risks, or start a free trial to explore it yourself.
.jpg)
.svg){kind=small}
