Autonomous agentic AI has a habit of going destructively rogue. It is unlikely we can stop this. Outerlimit has a solution focused on preventing any harm from rogue agents.
New York-based Outerlimit has emerged from stealth with $16 million pre-seed funding raised from AlbionVC, Evolution Equity Partners, Crane Venture Partners and a number of individual angel investors. Founded by Tony Pepper, Neil Larkins, and Peter Vincent, the firm offers a decentralized security and authorization layer to secure autonomous agentic AI.
Enterprises are rapidly adopting autonomous agents to increase the speed of decisions and automation of actions. As the underlying AI models become more powerful (but remain built on probability rather than known truth), these autonomous agents are used for more and more complex purposes with wide-ranging access to business identity and credentials. However, as the business purpose of agents becomes more complex, it becomes correspondingly more difficult to define and limit what autonomous actions they can take (a process known as alignment).
Historically, cybersecurity is based on curtailing the action of humans through laws (or rules) and the fear of consequences. In the wider world, this is legislation, and the fear of monetary fines or jail time is the consequence of breaking the law. In cybersecurity the ‘law’ is governance, and the consequence of defying governance is being fired. This has been the basis of security throughout history: the fear of consequences.
But it doesn’t work with autonomous agents. They have no concept of rules or morality (other than precisely what they have been instructed) nor any fear of consequences. Controlling autonomous AI agents can only be enforced by the internal precision of their coding (aligning capabilities with and limiting them to a defined purpose – and nothing else) and the effectiveness of external guardrails to limit misuse by outsiders. We are failing in both areas and our existing cybersecurity products or attitudes cannot help.
“In this new agentic era,” comments Vincent, “where agents use reasoning models with access to tools that can impact the world, they simply don’t operate within these human constructs. Agents can change their behavior based on what they read, or how they interact with other agents, while acting at machine speed. Harnessing this powerful intelligence requires a fundamentally new security architecture – one that binds identity, authorization, and action into a single operation at the moment of execution.”
Since we cannot and probably never will be able to control the AI we have built, security must shift focus from fear of consequences to prevention of harm. Outerlimit uses the tripartite concept of discover, observe and enforce to achieve this. It discovers (locates) agents in the system, it observes their behavior, and it enforces a pre-defined policy of allowed and disallowed autonomous actions.