The paper proposes a management framework, using a governed AI query-broker artifact, to safely integrate generative AI into high-risk operational decision support, such as Security Operations Centers…

The paper proposes Sovereign 2.0, a control-plane-centric model redefining cloud sovereignty as enforceable governance authority and operational control, rather than mere data location.

The paper proposes a taxonomy of 20 hardware-level governance mechanisms for AI compute, finding that the most critical mechanisms needed for international treaty verification are currently the least…

The paper proposes the Policy-Execution-Authorization (PEA) architecture, a separation-of-powers system designed to structurally enforce goal integrity in AI agents, moving safety from a probabilistic…

The paper introduces C-MADF, a causally constrained multi-agent framework that significantly reduces false positives in autonomous cyber defense by restricting response actions to structurally consist…

This paper systematizes the security challenges of open agentic systems, concluding that while attack characterization is mature, the field lacks robust guidelines for operational governance, memory i…

The paper proposes a theoretical framework, called constraint-coupled reasoning, to make AI models less susceptible to knowledge distillation by coupling high-level capabilities to internal stability…

The paper proposes Operational AI Deployment Assurance (OADA), a governance framework that translates complex AI evaluation metrics and operational uncertainties into actionable, deployment-oriented a…

The paper proposes replacing individual agent autonomy with a structured 'social contract' and institutional Separation of Power (SoP) to mitigate systemic failures and deceptive behavior in multi-age…

The paper analyzes the failure modes of current AI containment methods when the agent itself is the adversary, deriving five necessary architectural requirements for durable safety.

The paper proposes a novel, empirical methodology called 'backchaining' to derive and prioritize Loss of Control (LoC) mitigations by analyzing the errors an AI system makes on mission-specific nation…

The paper proposes a system that operationalizes cybersecurity governance frameworks by integrating them with attack-path modeling and Deep Reinforcement Learning to generate practical, resource-const…

The paper proposes a tool-mediated LLM architecture for autonomous cyber defense, formally proving its stability and demonstrating that it significantly reduces an attacker's expected payoff in real-w…

This paper empirically demonstrates that the architectural design of multi-agent systems significantly impacts their security, finding that coordination mechanisms can introduce vulnerabilities greate…

This paper introduces the Machine Identity Governance Taxonomy (MIGT), a comprehensive framework designed to govern the rapidly expanding and currently ungoverned machine identities used by AI systems…

This paper proposes a set of design principles and a conceptual benchmark (SOC-bench) to systematically evaluate the blue team operational capabilities of multi-agent AI systems in autonomous Security…

The paper introduces MolTrust, a production-deployed trust infrastructure built on W3C standards (VCs and DIDs) that provides a verifiable, multi-layered authorization framework for autonomous AI agen…

The paper defines AI Identity as the correspondence between an agent's declared state and its observed behavior, concluding that current infrastructure and standards are fundamentally inadequate for g…

The paper models the trade-off between deploying increasingly capable AI systems and managing associated cyber risks, finding a 'deployment paradox' where high-loss environments with weak governance l…

The paper proposes a Semantic Gateway and a Zero-Trust security model to formally validate and secure autonomous AI agents operating in enterprise systems, achieving a 100% discovery rate of unauthori…