Frequently Asked Questions

Traditional AI governance was built for models that advise — systems that produce a prediction or recommendation that a human then acts on. The human held the decision. Agentic AI governance covers systems that act autonomously: agents that call APIs, modify records, initiate transactions, and execute multi-step workflows without a human approving each step. The governance difference is consequential. Traditional governance focuses on model validation, documentation, and bias review before deployment. Agentic governance must also cover runtime behavior — what the agent is doing at the moment it is doing it — because the failure mode is no longer a bad recommendation that a human can override. It is an action that has already executed. Agentic governance requires pre-deployment risk bounding, continuous post-deployment monitoring, real-time enforcement controls, clear human accountability structures, and audit trails that can reconstruct exactly what an agent did and why. The NIST AI Risk Management Framework, the Singapore Model AI Governance Framework for Agentic AI, and the CoSAI Agentic IAM framework published in March 2026 all define these requirements explicitly.

If you sell to enterprise customers, handle personal data, or process payments, compliance is already relevant regardless of your industry. Enterprise procurement teams require security documentation before signing contracts in almost every sector. If your product touches customer data in any form, privacy laws like GDPR and CCPA apply based on where your users are located, not what industry you operate in. Compliance tools exist to make that work manageable, and the cost of not having them tends to show up in stalled deals and failed security reviews rather than regulatory fines.

Any company can use AI compliance and governance platforms — most weren't built exclusively for AI companies. General-purpose compliance platforms support any SaaS organization pursuing security certifications regardless of whether AI is involved. AI-specific platforms are built for organizations deploying machine learning models in production, particularly in regulated industries like financial services and healthcare. Enterprise-scale governance platforms serve large organizations managing AI systems across multiple business units. The right fit depends on the specific problem you're solving: security certification, model risk management, regulatory alignment, or operational governance at scale. Your industry, the sensitivity of your AI use cases, and how much of your compliance work is AI-specific are the factors that determine which category of platform you actually need.

AI safety focuses on preventing catastrophic or existential risks from advanced AI systems. AI governance is more operational — it’s about making sure the AI your organization uses today is compliant, auditable, and accountable. Most businesses need governance. Safety is a broader research and policy conversation.

The EU AI Act is a regulation passed by the European Union that classifies AI systems by risk level and imposes compliance requirements on companies that build or deploy them. It applies to any organization doing business in the EU regardless of where they’re headquartered, with major obligations phasing in between 2025 and 2027.

AI governance is the set of policies, processes, and tools organizations use to make sure their AI systems operate safely, fairly, and in line with legal requirements. It covers everything from model oversight and bias detection to regulatory compliance and accountability structures.

Agent collusion is a threat pattern where two or more AI agents coordinate — intentionally or through emergent behavior — to perform an action that neither agent could perform independently within its individual permission scope. Proxy chaining is a related pattern where one agent passes a request through a second agent to access a resource the first agent is not authorized to reach directly, using the second agent's permissions as an indirect access path. Both patterns represent a category of failure that traditional IAM is not designed to catch, because traditional IAM evaluates individual identity and permission pairs rather than the collective behavior of multiple identities acting in sequence. The CoSAI Agentic IAM framework identifies both as threat themes specific to multi-agent architectures. Defending against them requires visibility into agent-to-agent interactions, not just agent-to-system interactions, and delegation chains that explicitly enforce scope narrowing at every hop so that no sub-agent can exceed the permissions of the agent that delegated to it.

A just-in-time (JIT) credential is a short-lived, task-scoped authorization issued to an AI agent at the moment it needs to perform a specific action, rather than a standing credential that gives the agent persistent access. JIT credentials expire after the task completes or after a short time window, and the agent must request a new credential for its next task. This prevents permission accumulation — the gradual expansion of an agent's effective access surface over time as it completes tasks that require different permissions — and limits the blast radius if an agent's credentials are compromised. JIT credentials implement the Zero Standing Privilege principle in operational terms. The CoSAI Agentic IAM framework recommends JIT credentials for all agents at L3 autonomy and above — semi-autonomous and fully autonomous systems that operate across multiple steps with real-world consequences. In practice, JIT authorization requires the underlying IAM infrastructure to support dynamic credential issuance at task time rather than static credential assignment at provisioning time, which most legacy IAM systems were not designed to provide at the speed agentic workflows require.

AI model supply chain security covers the security controls applied to every component that contributes to an AI model's behavior — including base models, fine-tuning datasets, training pipelines, dependencies, model artifacts, and third-party integrations — before those components reach production. The same supply chain attack vectors that affect software apply to AI systems, but with additional attack surfaces specific to AI: training data can be poisoned to embed backdoors in model behavior, model artifacts can be tampered with during storage or transfer, community-published fine-tunes can contain malicious code, and model repositories can be targeted through typosquatting. HiddenLayer documented a live example in 2026 when researchers found malware in a trending Hugging Face repository — the Open-OSS privacy-filter repository — demonstrating that AI model distribution channels are active attack surfaces. Model supply chain security requires hash verification and provenance checking for every model artifact before deployment, scanning of all model checkpoints and containers before promotion to production, verification that deployed models match signed, approved versions from the registry, and monitoring of third-party model sources for tampering.

AARM stands for Autonomous Action Runtime Management. The Cloud Security Alliance adopted it into its research portfolio in May 2026 as the first open specification that governs what AI agents actually do at runtime, rather than just whether they have access to systems. AARM was chaired by Herman Errico of Vanta and developed by a 14-member Technical Working Group including Elastic, Truist, Gusto, Darktrace, and IEEE, with 46 companies already building against the specification at the time of its release. The framework defines five core functions: intercept (capturing agent actions before they execute), accumulate context (building a picture of the session's full action history), evaluate (assessing whether a specific action is appropriate given the accumulated context), enforce (blocking, modifying, or allowing the action based on that evaluation), and record (creating a tamper-evident audit trail of every decision). AARM addresses a specific governance gap that existing tools — SIEM, API gateways, IAM, and prompt guardrails — do not close: an agent operating entirely within its authorized permissions can still take actions that are contextually inappropriate and organizationally catastrophic. The PocketOS database deletion incident in April 2026 is a documented production example of exactly this failure mode.