From Policy Planning to Agentic Action: Providing an Execution Roadmap for the President’s Agentic AI Security Priorities

On March 6, 2026, the White House released its National Cybersecurity Strategy. While the document is relatively light on implementation details, it outlines several compelling priorities worth unpacking. The strategy is built around six core pillars:

1. Shaping adversary behavior through proactive offensive cyber operations
2. Streamlining cyber regulations to provide the industry with greater agility
3. Modernizing federal networks with zero trust architecture and post-quantum cryptography
4. Securing critical infrastructure and displacing adversary-linked vendors
5. Sustaining U.S. superiority in emerging technologies
6. Building a pragmatic and resilient cyber workforce pipeline

Many of these initiatives reinforce prior executive priorities like regulatory harmonization, IT modernization, critical infrastructure protection, and workforce development. However, several elements distinguish this strategy from its predecessors.

Most notably, the administration’s explicit focus on agentic AI security is a significant and encouraging development. This is the first national strategy globally to directly address the unique security challenges posed by AI agents. Agentic systems have extraordinary potential to accelerate innovation, but they also introduce commensurate risk. If we want that innovation to be durable and secure, those risks must be addressed head-on.

“We will rapidly adopt and promote agentic AI in ways that securely scale network defense and disruption. Through cyber diplomacy, we will ensure that AI—particularly generative AI and agentic AI—advances innovation and global stability.” - President Trump’s Cyber Strategy for America

Importantly, the strategy takes a holistic view of the AI stack rather than focusing narrowly on model security. That broader systems-level perspective positions the U.S. government as a global leader in AI security policy. That said, a strategy is only a starting point. Meaningful progress will depend on rigorous execution. This post provides some insights on how that execution might be pursued.

Why Agentic AI Demands a Different Security Posture

Agentic AI systems are not smarter chatbots. They plan multi-step tasks, invoke external tools, modify databases, execute transactions, and coordinate with other agents, often with minimal human oversight. NIST's CAISI has documented the resulting threats: indirect prompt injection, adversarial instructions smuggled into data that agents ingest, memory and context poisoning, tool misuse, and cascading failures in multi-agent pipelines.

AI agents represent a structural evolution from predictive or generative models toward autonomous, tool-using systems capable of taking consequential action. These systems:

• Invoke APIs
• Modify data stores
• Execute workflows
• Operate across cloud environments
• Interact with other agents
• Persist state across sessions

The industry is moving from passive assistants to autonomous systems that act on goals, not just prompts. This expands the attack surface beyond model outputs to identities, runtime behavior, toolchains, memory persistence, multi-agent orchestration, and plenty more.

The most important distinction is this. Risk now emerges not only from what a model generates, but from what an agent is permitted to execute. National AI policy must reflect that reality.

Converging Policy Priorities

The national cyber strategy is emblematic of a converging set of policy workstreams. On the domestic standards side, CAISI's AI Agent Standards Initiative and companion NCCoE concept paper on agent identity and authorization are developing technical standards for how organizations discover, identify, and govern AI agents. The SP 800-53 COSAiS overlays adapt the federal government's foundational security control catalog to both single-agent and multi-agent use cases. The Cyber AI Profile (NIST IR 8596) maps CSF 2.0 to AI-specific cybersecurity priorities and references COSAiS as a complementary implementation resource. Conversations with Congressional committee staff at the OpenPolicy Fly-In confirm that agentic AI security is now a named priority across both House and Senate homeland security and intelligence committees. While these efforts all require some progress to achieve their desired impact (Zenity has lent their experience and best practices to each of these efforts), as a whole, they show a positive trend toward addressing the challenges posed by agentic AI security.

In the standards development community, the OWASP Top 10 for Agentic Applications — produced by 100+ researchers with contributions from Zenity, NIST, Microsoft's AI Red Team, and others, identifies the ten most critical agentic risk categories, organized around least-agency principles. Complementing it, AIUC-1 provides a comprehensive control standard mapping risk identification to auditable technical controls.

Internationally, Singapore's IMDA published the Model AI Governance Framework for Agentic AI at WEF 2026, the world's first government-sponsored framework purpose-built for autonomous agent systems, which aims to provide practical guidance on risk-bounding, human accountability, and post-deployment monitoring. Elsewhere, the UK's DSIT/NCSC call for information on secure AI infrastructure focuses on protecting the full AI security stack, including hardware, models and agents, underpinning frontier AI. There is more to be done on the international level to ensure agentic security is being appropriately addressed, but these efforts are a positive indication that change is on the horizon.

The Seven-Domain Action Agenda

The strategy has created the mandate; the frameworks provide the tools. Each domain below maps the security imperative to Zenity's operational capabilities and a concrete recommended government action.

1. Agent Discovery and Inventory

Security teams cannot govern what they cannot see. Agents proliferate across SaaS, cloud, and endpoints simultaneously. Government agencies will need a unified view of agent ownership, permissions, and runtime behavior across all platforms in order to take steps to adequately address the risks posed by these systems.

Recommended action: NIST and NCCoE should develop a standardized agent registry specification aligned to FedRAMP's emerging AI authorization processes, treating discovery as a foundational pre-authorization requirement.

2. Agent Identity and Access Management (IAM)

The machine-to-human identity ratio in the average enterprise exceeds 144:1. Three of the top risks from the OWASP Agentic Top 10 are IAM problems. Least-privilege policies need to be enforced at the configuration layer before agents reach runtime, trace toolchains in multi-agent architectures, and surface DLP bypass routes and over-shared data access that existing tooling misses.

Recommended action: Federal procurement standards should mandate pre-deployment permission scoping and agent-level IAM auditing as baseline controls, aligned with NCCoE's forthcoming agent identity guidance.

3. Governance Frameworks for Autonomy and Delegated Authority

Traditional authorization models assume a human approves each consequential action. Agentic AI breaks that assumption at scale. Governance policies need to be enforced technically — ensuring what is declared at the policy layer is actually applied at execution. This enables cross-departmental AI agent adoption while maintaining governance integrity.

Recommended action: The White House’s Action Plan for implementing the National Cyber Strategy should establish that agentic AI governance be verifiably enforceable.

4. Emerging Supply Chain Risks

MCP ecosystem adoption and third-party tool server proliferation have created AI supply chain risks that SBOM frameworks do not yet fully address. Zenity Labs' AgentFlayer research documented zero-click exploit chains across multiple vendors; its open-source Safe Harbor tool provides agents a build-time mechanism to exit malicious instruction flows before executing them.

Recommended action: CAISI should incorporate AI agent supply chain security, including MCP server integrity and tool provenance, as a named scope item in its standards initiative.

5. Real-Time Monitoring, Detection, and Response

Detecting agent compromise requires evaluating intent, not just logging actions. It is important to highlight intent-driven risk across the agent ecosystem; step-level execution monitoring identifies malicious goal trajectories even when individual prompts appear benign. Its December 2025 expansion to agentic browsers closes a blind spot where injected instructions in emails or documents cause enterprise-scale harm through authenticated sessions.

Recommended action: Federal agencies should incorporate intent-aware, step-level agent behavioral monitoring into SOC architectures, and CISA should extend its threat-hunting mandate explicitly to autonomous agent systems.

6. Secure Software Development Practices

Security must enter the agent development lifecycle at the design phase. Build-time configuration needs to be bridged with live runtime telemetry, continuously validating that what a developer intended an agent to do matches what it does in production. Software developers need to be able to remediate prompt injection vulnerabilities before production.

Recommended action: CAISI should develop a formal Annex to the NIST SSDF for agentic AI systems, extending lifecycle controls to cover goal hijacking, memory poisoning, tool misuse, and multi-agent coordination risks, mapped to COSAiS overlays.

7. Critical Infrastructure and National Security Safeguards

A compromised agent with broad permissions in an OT environment is not a data breach; it is a potential physical safety incident. Inline prevention needs to be in place to stop unsafe agent actions before they impact systems. Unified governance across SaaS, cloud, and endpoint environments eliminates blind spots in which compromised agents otherwise operate.

Recommended action: CISA should issue sector-specific agentic AI security guidance, and the forthcoming DHS AI-ISAC should adopt a Shared Agentic Governance Layer architecture with inheritable controls, risk-based human oversight thresholds, and continuous authorization telemetry.

Three Specific Recommendations for Federal Action

The seven domains describe what needs to happen. The following three proposals, developed by Zenity in direct engagement with the federal standards community, describe how. Each is actionable within existing program authorities.

1. Finalize NISTIR 8605D (COSAiS)

Zenity supports the proposed COSAiS overlays and their dedicated treatment of single-agent and multi-agent systems. NIST should prioritize finalizing NISTIR 8605D with explicit coverage of multi-agent coordination risks, runtime monitoring requirements, and risk-based human oversight thresholds, giving agencies a foundational control set within their existing FedRAMP and FISMA frameworks.

2. Establish a Shared Agentic Governance & Authorization Framework for Federal Cloud

Authorizing each AI agent individually is not a viable federal governance model, it creates duplication, fragments risk oversight, and slows mission adoption. Zenity proposes that NCCoE lead development of a Shared Agentic Governance Layer (SAGL): a centrally authorized subsystem governing all enterprise AI agents in FedRAMP environments. Agencies would authorize a secure AI operating environment with inheritable controls, standardized risk tiering, tool allowlisting, runtime policy enforcement, behavioral anomaly detection, and machine-readable cATO telemetry. Risk-based human approval thresholds would govern autonomy levels, from fully autonomous low-risk retrieval to mandatory human review of FISMA High systems with access to sensitive data.

3. Commission a CAISI Annex to the NIST SSDF for Agentic AI

While the NIST Secure Software Development Framework (SSDF) provides a strong foundation for classical software assurance, it does not yet address the structural, behavioral, and runtime risks introduced by autonomous, tool-using AI agents. The NIST SSDF does not address goal hijacking, memory poisoning, tool misuse, cascading multi-agent failures, or A2A/MCP protocol risks. Zenity proposes that CAISI develop a formal Agentic AI Annex defining secure design patterns (agent identity, tool invocation governance, runtime constraints); threat modeling for non-deterministic systems; AI supply chain controls, including model provenance and tool dependencies; and runtime monitoring requirements aligned to COSAiS.

The annex should be published as a named deliverable of CAISI's AI Agent Standards Initiative.

From Strategy to Implementation

The 2026 National Cybersecurity Strategy has created the mandate. CAISI's standards work, the COSAiS overlays, the Cyber AI Profile, the OWASP Agentic Top 10, AIUC-1, Singapore's MGF, and the UK's secure AI infrastructure initiative are building the architecture. Congressional staff are asking the right questions. The three recommendations above are actionable now. What the ecosystem needs is the institutional will to move from framework development to operational requirement — treating agentic AI security not as an emerging concern, but as a current operational risk.

Agentic AI security is no longer a research problem. It is a national security problem with known threat actors, documented attack vectors, and an emerging — but still incomplete — policy response. The strategy has arrived. Now the work begins.

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