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AAT-36.13: Emergent Behavior & Collusion Protections

AAT 5 — Medium Protect

Mechanisms exist to detect and contain emergent or collusive behaviors among multiple Artificial Intelligence (AI) and Autonomous Technologies (AAT), including: (1) Automated or human-triggered containment; and (2) Formal investigation to determine the root cause of agentic cascades or collusion.

Renumbered: Formerly AAT-16.13 in SCF 2025.4.

Control Question: Does the organization detect and contain emergent or collusive behaviors among multiple Artificial Intelligence (AI) and Autonomous Technologies (AAT), including: (1) Automated or human-triggered containment; and (2) Formal investigation to determine the root cause of agentic cascades or collusion?

Capability Maturity Model

Level 0 — Not Performed

Practices are non-existent, based on the inability to demonstrate an implemented and operational capability. A reasonable person would conclude the control is not being performed.

Level 1 — Performed Informally

SCR-CMM Level 1 criteria definitions are not available for this control: ▪ A reasonable person would conclude this control requires a structured process. ▪ At this level of maturity, the "ad hoc" nature of performing a capability informally would indicate the intent of the control is not met due to a lack of consistency and formality.

Level 2 — Planned & Tracked

Artificial Intelligence and Autonomous Technology (AAT) capabilities are requirements-driven, but are not standardized across the entity (e.g., local/regional level consistency). Capability criteria associated with this control reasonably expect the following criteria to exist: ▪ Policies and standards associated with AAT domain capabilities are formally documented and centrally-managed by the entity. ▪ Standardized Operating Procedures (SOP) associated with AAT domain capabilities are documented and maintained by process owners. ▪ IT and/or cybersecurity personnel work with business stakeholders and process owners to appropriately scope and reasonably implement cybersecurity and data protection controls associated with AAT domain capabilities to address applicable statutory, regulatory and/or contractual requirements for Technology Assets, Applications, Services and/or Data (TAASD). ▪ Artificial Intelligence (AI)-related controls are primarily administrative and preventative in nature (e.g., policies, standards and procedures & guidelines). ▪ Asset management may be a defined function (e.g., team or department) or assigned as an additional duty to existing IT and/or cybersecurity personnel.

Level 3 — Well Defined

Artificial Intelligence and Autonomous Technology (AAT) capabilities are standardized across the entity for applicability to People, Processes, Technologies, Data and/or Facilities (PPTDF) to ensure consistency for Technology Assets, Applications, Services and/or Data (TAASD). Capability criteria associated with this control reasonably expect the following criteria to exist: ▪ Policies and standards associated with AAT domain capabilities are formally documented and centrally-managed by the entity's Governance, Risk & Compliance (GRC) team, or similar function. ▪ Standardized Operating Procedures (SOP) associated with AAT domain capabilities are well-documented and kept current by process owners. ▪ An Artificial Intelligence Governance (AIG) team, or similar function, is appropriately staffed and supported to implement and maintain AAT domain capabilities. ▪ Technology is leveraged to enhance the efficiency and accuracy of AI governance, risk management and compliance operations (e.g., dedicated AI governance platform). ▪ The entity's Governance, Risk & Compliance (GRC) team, or similar function, works with business stakeholders and process owners to appropriately scope and reasonably implement cybersecurity and data protection controls associated with AAT domain capabilities to address Minimum Compliance Requirements (MCR) (e.g., applicable statutory, regulatory and/or contractual requirements) and Discretionary Security Requirements (DSR) (e.g., entity-required controls). ▪ An implemented and operational capability exists to detect and contain emergent or collusive behaviors among multiple AAT, including: (1) Automated or human-triggered containment; and (2) Formal investigation to determine the root cause of agentic cascades or collusion.

Level 4 — Quantitatively Controlled

Utilize SCR-CMM Level 3 criteria definitions: ▪ There are no defined Level 4 criteria, since it is reasonable to assume a quantitatively-controlled process is not necessary to operationalize this control. ▪ While it may be possible to develop “metrics-driven” capabilities for this control, the criteria would be organization-specific to define.

Level 5 — Continuously Improving

Utilize SCR-CMM Level 3 or Level 4 (if available) criteria definitions: ▪ There are no defined Level 5 criteria, since it is reasonable to assume a continuously-improving process is not necessary to operationalize this control. ▪ Level 5 capabilities should be considered “world-class” where the control builds on Level 4 capabilities, but are continuously improving through Artificial Intelligence (AI) and/or Machine Learning (ML) technologies. ▪ While it may be possible to develop responsive capabilities for this control through the use of AI and/or ML technologies, the criteria would be organization-specific to define.

Assessment Objectives

  1. AAT-36.13_A01 the organization has the ability to detect emergent or collusive behaviors among multiple Artificial Intelligence (AI) and Autonomous Technologies (AAT).
  2. AAT-36.13_A02 the organization has the ability to contain emergent or collusive behaviors among multiple AAT through automated or human-triggered means.
  3. AAT-36.13_A03 formal investigation determines the root cause of agentic cascades or collusion.

Technology Recommendations

Micro/Small

  • Monitor multi-AI deployments for unexpected behavior

Small

  • Policy to review interactions between AI systems

Medium

  • Formal procedure to detect emergent/collusive AI behavior
  • Containment procedures

Large

  • AI behavior monitoring tools
  • Isolation procedures for compromised AI agents

Enterprise

  • Enterprise AI behavior monitoring platform
  • Automated detection of multi-agent collusion
  • AI sandboxing and isolation capabilities

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