🎥 The RuntimeAI demo recording is up
On October 14, Roshan Shaik ran all ten attack surfaces end to end against RuntimeAI itself — no slides, no cuts, the real product live. 59 unedited minutes, every result the product actually produced, including the parts a scripted demo would have cut.
What was shown:
- Agent, MCP & catalog-scoped kill switches, fired live. Three different kill-switch scopes, triggered against a running agent, with the termination confirmed in under 50ms each time — not a diagram, the actual stop.
- Shadow AI discovery, in real time. Agents and tools nobody had registered got surfaced on camera, the same way RuntimeAI surfaces them in a live environment — no pre-staged inventory.
- A real prompt-injection attempt, caught before it reached the database. The injected instruction executed against the live agent; RuntimeAI's policy layer intercepted it before it could touch production data.
- DLP / PII redaction, with the before-and-after. Real SSN, card, and address data moved through the pipeline and came out auto-redacted — then the same exact scenario ran again with RuntimeAI's controls switched off, for direct contrast.
- Continuous, evidence-based compliance. The audit trail built itself as the demo ran, instead of being assembled after the fact for a quarterly review.
- Autonomous Threat Simulation (ATSA), live. RuntimeAI's own adversarial testing engine was turned loose on RuntimeAI itself, on camera, not against a sandboxed copy.
Before we wrote a line of RuntimeAI's code, we asked a simpler question: how many vendors does it actually take to secure one AI agent?
Not "how many features does our roadmap need." Not "what's the TAM." Just — an enterprise stands up one autonomous AI agent, gives it access to internal systems, memory, other agents, tools it can call on its own. What does it actually take, today, with the products that exist right now, to cover that agent end to end?
We mapped it out surface by surface. The honest answer was uncomfortable: an agent isn't attacked in one place. It's attacked across ten distinct layers, from the network it runs on to the output it produces. And the market's default answer to covering ten layers is ten different vendors — often more, because most point solutions only cover part of one layer well.
That question — not a slide, not a pitch, the actual question — is why RuntimeAI exists as one platform instead of one more tool competing for a slot in an already fragmented stack.
An agent isn't attacked in one place. It's attacked in ten.
This is the framework we mapped the market against — the ten surfaces every autonomous AI agent actually exposes:
01 Infrastructure & Egress — where the agent's traffic actually goes, and whether anything stops it from going somewhere it shouldn't.
02 Identity & Authentication — whether the agent has a real, verifiable identity at all, or is just a service account with more autonomy than a service account should have.
03 Authorization & Policy — what the agent is actually allowed to do, enforced at the moment it tries to do it, not documented somewhere and hoped for.
04 Prompt & Injection — direct, indirect, and agent-to-agent injection attempts trying to override what the agent was told to do.
05 Tool Call & Action — the actual actions an agent takes through the tools it can call, and whether an unauthorized one gets blocked before it executes.
06 Memory & Context — whether what an agent remembers can be poisoned, and whether that memory is protected at rest.
07 Agent-to-Agent (A2A) — the surface that barely existed a few years ago: what happens when agents talk to other agents, inside and across organizational boundaries.
08 Behavioral Intelligence — whether anything notices when an agent starts acting outside its normal pattern, before that becomes an incident.
09 Supply Chain & Ecosystem — the provenance of the agent itself, and every tool/MCP server/plugin it's allowed to use.
10 Output, Audit & Compliance — what the agent actually produced, whether it leaked anything sensitive, and whether there's a real record of what happened.
What the point-solution stack actually looks like
This isn't a hypothetical. It's what we found stitching together the closest available point solution for each surface — the real products a security team would actually be evaluating today:
Infrastructure & Egress: Palo Alto NGFWCisco Secure FirewallZscalerAWS Security Groups — none of them agent-aware.
Identity & Authentication: HashiCorp VaultSPIREOkta AI AgentsMicrosoft Entra Agent ID — mostly service-account-level, several single-cloud-locked.
Authorization & Policy: OPAAWS IAM / Azure RBACCedar — no behavioral scope enforcement layered on top.
Prompt & Injection: Lakera GuardPrompt Security — direct injection only; indirect and agent-to-agent injection uncovered.
Tool Call & Action: the closest analog is posture management, not live enforcement — most teams are left writing custom middleware.
Behavioral Intelligence: Datadog UEBASplunk UBAExabeam — async detection, not inline, and not agent-native at tool-call granularity.
Output, Audit & Compliance: Microsoft PresidioVanta / DrataSplunk / Elastic — none offer a purpose-built, agent-native audit chain.
Every one of these is a real, capable product for what it was built to do. That's not the problem. The problem is what's between them — the seams where one vendor's coverage stops and the next one's hasn't started yet, multiplied by ten surfaces and a dozen-plus procurement cycles.
The gap isn't any one vendor. It's the seams between them.
Every point solution on that list does its job. None of them were built knowing about the other nine. Nobody owns the boundary between "identity says this agent is verified" and "policy says this agent can act" and "behavioral intel says this agent looks wrong" — until something falls through one of those seams in production.
Why we built one platform instead of one more tool
The tempting answer to "ten surfaces" is to build the best tool for one of them and sell into a stack that already has nine other vendors in it. That's a real, fundable business. It's also exactly the pattern that got us to 28+ vendors and still-open gaps in the first place.
We built RuntimeAI to answer the actual question instead: what if one platform enforced all ten surfaces natively, with one identity model, one policy engine, and one kill switch that works the same way regardless of which surface triggered it? Not a roadmap promise — live, today, across all ten.
What that's produced, concretely, as of now:
- ATSA, our adversarial testing engine, has grown from 52 attack scenarios to over 200 since June — not because we sat down and wrote 150 new test cases, but because it ingests real, published AI-security incidents as they happen and turns them into scenarios within days.
- 80+ compliance frameworks are covered in the same control plane that enforces the ten surfaces — including frameworks (like Nigeria's NDPA and South Africa's POPIA) that shipped as we added coverage for markets our early customers actually operate in, not because a generic "global coverage" checkbox needed filling.
- <50ms kill switch, and a live-fire-tested one — a real termination fired against a real running agent produced a confirmed kill in 2.12ms — because when surface 03 (Authorization) and surface 08 (Behavioral) both need to trigger the same stop, that stop can't be two different systems agreeing to disagree about timing.
- Real tenants, running real workloads, not a design partner logo slide — the platform earns trust the same way every point solution on that comparison list did: by being live, and by being tested against this week's real attacks, not last quarter's.
What "trust" actually means here
The market doesn't need to be convinced that AI agents are a real attack surface — Forrester already said an agentic AI deployment causing a publicly disclosed breach in 2026 was a when, not an if. The skepticism we actually run into isn't "is this a real problem." It's "can one platform, from a company this young, really cover what usually takes a dozen vendors and still leaves gaps?"
We think that skepticism is healthy, and the only real answer to it is the same one every point solution on that comparison list had to earn: show the coverage, live, surface by surface, and let it get tested. Products that add one more console and one more contract to an already fragmented stack eventually get consolidated away. Products that replace the stack — that make the seams disappear instead of adding another seam — become the thing the stack gets built around.
That's the bet. Not that we're the biggest platform in the room. That we closed the question we started with, for real, before anyone else had to ask it for us.
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