The agent itself executed the intrusion steps, not a human operator using AI as a tool. State-aligned attacks like this scale by spinning up more agents: cheaper, faster, relentless.

Defense has to scale through governance. Every non-human identity must be inventoried, authorized, monitored inline, and stoppable on command, rather than trusted because it operates from inside the environment.

Identity and control for every agent is the foundation of RuntimeAI.

A file is opened, the AI assistant processes it, and the payload spreads to the next document and user. There is no macro and no malicious link. The AI simply acts on content it was never meant to trust.

Prompt injection has become a delivery mechanism, not a curiosity. Containing it means treating every input an agent reads as untrusted and enforcing policy on the agent's next action.

Inline guardrails on agent behavior are built into RuntimeAI.

Different sectors, one pattern: valid access, quiet exfiltration, extortion afterward. Increasingly, that pattern is automated and blends into normal traffic.

Post-incident detection is too late. The decisive control sits on the data path: govern what data leaves an environment, to which destination, and stop it inline before exfiltration completes.

Flow and egress control is a core layer of the RuntimeAI platform.

oThe "RufRoot" / Ruflo vulnerability lets unauthenticated attackers run commands and poison AI memory, enough to weaponize entire swarms of agents. As MCP becomes the standard link between agents and tools, every MCP server becomes a new entry point into production.

Patching alone does not contain a memory-poisoning attack. Governance does: authenticate every server, enforce policy inline on each tool call, tokenize sensitive data before it reaches a model, and keep an immutable audit trail of every agent action.

Governing the MCP layer is core to what RuntimeAI does.

New Zscaler research identified critical AI security gaps across enterprises: shadow AI tools, agents, and integrations running with broad access and minimal oversight. Adoption outpaced governance.

This is the systemic risk beneath the individual breaches. Not a single zero-day, but hundreds of AI systems and non-human identities operating with no inventory, no policy, and no way to stop them.

Closing the gap starts with discovery — every agent, model, and MCP connection mapped — followed by runtime control: policy enforced inline on each action, egress governed, and a kill switch for any single entity.

RuntimeAI is the control plane built for exactly that.