A real-world example shows an agent correctly denying a request for a specific company's data but leaking other firms' data on a generic prompt. This highlights that agent security isn't about blocking bad prompts, but about solving the deep, contextual authorization problem of who is using what agent to access what tool.

States and corporations will not permit citizens to have AIs that are truly aligned with their personal interests. These AIs will be hobbled to prevent them from helping organize effective protests, dissent, or challenges to the existing power structure, creating a major power imbalance.

Simply giving an agent a user account is dangerous. An agent creator is liable for its actions, and the agent has no right to privacy. This requires a new identity and access management (IAM) paradigm, distinct from human user accounts, to manage liability and oversight.

Who owns an employee's personalized AI agent? If a tech giant owns this extension of an individual's intelligence, it poses a huge risk of manipulation. Companies must champion a "self-sovereign" model where individuals own their Identic AI to ensure security, autonomy, and prevent external influence on their thinking.

Unlike centralized models from major labs, decentralized AI agent collectives like 'Moltbook' lack a single entity responsible for safety or alignment. There is no central authority to appeal to if the system's emergent behavior becomes harmful, creating a critical governance challenge for the AI safety community.

Despite their sophistication, AI agents often read their core instructions from a simple, editable text file. This makes them the most privileged yet most vulnerable "user" on a system, as anyone who learns to manipulate that file can control the agent.

Instead of relying solely on human oversight, AI governance will evolve into a system where higher-level "governor" agents audit and regulate other AIs. These specialized agents will manage the core programming, permissions, and ethical guidelines of their subordinates.

With frontier models, creators deny responsibility for user applications, while users claim no control over the model's inner workings. Sovereign AI eliminates this gap. By controlling the entire stack, an organization becomes fully accountable, satisfying regulators who need proof of what an AI did and why.

Unlike traditional software, AI products have unpredictable user inputs and LLM outputs (non-determinism). They also require balancing AI autonomy (agency) with user oversight (control). These two factors fundamentally change the product development process, requiring new approaches to design and risk management.