arXiv:2606. 12320v1 Announce Type: new Abstract: Enterprise security was built to govern data boundaries: the protected surface was data at rest and in transit, and the controls -- access control, data-loss prevention, perimeter inspection -- governed crossings of that boundary.
arXiv:2608. 06510v1 Announce Type: cross Abstract: Agentic AI promises a more flexible form of digital agency: systems that can act on users' behalf, from filtering content to negotiating prices to selecting services.
The report introduces an analytical framework for assessing risks that arise when AI agents interact, especially as those interactions cross organisational boundaries. It defines three deployment tiers—singular governance, federated governance, and open environments—each with distinct governance requirements. For each tier, the report examines risk factors, failure modes, and controls, identifying who can apply them and highlighting gaps that require collective action.
arXiv:2608. 06510v2 Announce Type: replace-cross Abstract: Agentic AI promises systems that can act on users' behalf, from filtering content to negotiating prices to selecting services.
arXiv:2608. 10153v1 Announce Type: new Abstract: Enterprises are deploying autonomous AI agents faster than they can govern them, and prevailing approaches stretch a single discipline, typically DevSecOps built for deterministic automation, across every scale of agency.
arXiv:2608. 09025v1 Announce Type: new Abstract: Financial agents can turn correct context into an unauthorized effect: a customer-facing commitment, trade, or deployed policy.
arXiv:2607. 13040v1 Announce Type: cross Abstract: This paper examines where final authority should sit once capable AI systems are embedded in organizational workflows.
arXiv:2608. 16055v1 Announce Type: new Abstract: Existing agent benchmarks ask whether the agent finished the task.
The paper introduces Aegis, a runtime governance system for agentic AI that treats model outputs as action proposals and mediates them through a trusted decision layer before tool execution. Aegis evaluates proposals against active policy, resolves provenance server‑side, fails closed under uncertainty, and routes selected cases through a Senate‑style settlement process. In a sandbox evaluation across 6,300 rows, Aegis prevented all governed mock‑tool applications and risky side‑effect completions, preserving provenance and quorum evidence for all settled cases.
arXiv:2608. 11344v1 Announce Type: cross Abstract: Financial institutions are delegating consequential decisions to agentic AI systems that decompose goals, coordinate models and tools, and act with little oversight.
The paper introduces Governance-as-Code (GaC), a framework that translates the EU AI Act’s technical requirements into 43 machine‑checkable acceptance criteria across six compliance modules. GaC runs within a CI/CD pipeline, producing Article‑indexed audit evidence and providing actual Rego policy code. The authors validate GaC on two enterprise deployments, showing it reproduces manual audit findings—including three penalty‑triggering violations—while reducing audit labor by about 75%.
arXiv:2606. 17114v1 Announce Type: cross Abstract: AI agents are increasingly being adopted in enterprise and personal settings with access to emails, databases, documents, and other tools where they can read, update, and disseminate sensitive information.