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Fighting AI-based threats requires government and industry to combine what each can see and do: public agencies can contribute threat intelligence and coordination, while AI developers and technology operators understand the systems and infrastructure they build and run. Working together can improve awareness, vulnerability response, and resilience—but the available announcements and policy statements do not prove that partnerships alone prevent attacks.

What counts as an AI-based threat?

The phrase covers two connected but distinct challenges. One is protecting AI itself: the models, training data, capabilities, and related systems involved in developing and operating AI. The other is malicious use of AI in cyber activity. These concerns overlap, but they are not interchangeable; protecting a model is different from using AI-enabled tools to defend against an attack.

The NSA’s Artificial Intelligence Security Center defines AI security as protecting AI systems from “learning, doing, and revealing the wrong thing.” Its description emphasizes the AI lifecycle and the risks of digital attack, theft, or damage: NSA Artificial Intelligence Security Center. A June 2026 White House order also addresses improving access to cyber tools and services, linking AI security to defensive capability as well as the security of AI systems themselves: White House executive order.

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Why government and industry need each other

The case for partnership rests on complementary knowledge, not on a claim that any one arrangement has already been shown to reduce attacks. Government agencies may have threat intelligence, public-sector coordination responsibilities, and visibility across organizations. AI developers and infrastructure operators have detailed technical knowledge of their models, software, and systems, including how those systems are deployed and where vulnerabilities may surface.

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The Information Technology Industry Council (ITI) argues that combining these distinct information sets can give a more complete view of AI-related security threats and help organizations manage risk. That is an industry policy position, not a measured causal estimate: ITI AI Cybersecurity Policy Recommendations. The NSA likewise describes collaboration with industry, academia, intelligence, and other government partners as part of its AI security mission.

In practice, the potential value is a shorter path from finding a problem to understanding who is affected and coordinating a response. A company may identify a flaw in a model or software component; government partners may help connect that finding to broader threat information or other affected operators. Neither side necessarily has the complete picture alone.

How public-private collaboration works

Sharing incident and vulnerability information

On January 14, 2025, CISA announced its JCDC AI Cybersecurity Collaboration Playbook. The playbook sets out voluntary processes for sharing information about AI-related cybersecurity incidents and vulnerabilities among government, industry, international partners, and other stakeholders. The announcement also describes protections for sharing and the actions CISA may take after receiving information: CISA JCDC AI Cybersecurity Collaboration Playbook.

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Voluntary sharing matters because organizations may hesitate to disclose a vulnerability or incident without clarity about how information will be handled. The playbook’s stated protections are specific to that sharing process; they should not be assumed to apply identically to every other government-industry initiative.

Coordinating vulnerability discovery and fixes

A June 2, 2026 White House executive order directed the Treasury Department, NSA, and CISA to form an AI cybersecurity clearinghouse within 30 days, in voluntary collaboration with AI industry and critical-infrastructure operators. The directed work includes coordinating and deconflicting software vulnerability scans, discovering and validating vulnerabilities, and coordinating prioritized remediation and patch distribution: White House executive order.

The order establishes a directive and deadline, not proof that the clearinghouse is operational. Its model also differs from CISA’s playbook: the playbook describes voluntary incident and vulnerability information-sharing processes, while the order assigns a proposed clearinghouse a coordinated vulnerability-management role.

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Creating practical guidance for a specific sector

The Treasury Department’s Artificial Intelligence Executive Oversight Group (AIEOG) offers a financial-sector example. In an announcement dated February 18, 2026, Treasury said the partnership— involving the Financial and Banking Information Infrastructure Committee and the Financial Services Sector Coordinating Council—brought financial institutions and federal and state regulators together to develop practical AI cybersecurity and risk-management resources. The topics included AI governance, data practices, transparency, fraud, and digital identity. Treasury said six resources were planned for release during February; the announcement establishes that plan, not the eventual publication or impact of all six resources: U.S. Treasury announcement.

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Treasury described the goal as supporting secure, resilient AI use in the U.S. financial system, with resources intended to be practical rather than prescriptive. The approach illustrates how a sector can translate broad security concerns into guidance relevant to its own institutions and regulators.

Coordinating across critical sectors

Canada’s Canadian Cyber Defence Collective Strategic Forum (CCDC-SF), convened by Public Safety Canada and the Canadian Centre for Cyber Security, brings together government and private-sector leaders from information and communications technology, energy, food, and transportation. Its September 29, 2026 announcement says the forum addresses evolving cyber threats, including AI-associated threats, by combining government intelligence with industry expertise. It was scheduled to meet at least twice each year, with outcomes summarized in annual reports: Public Safety Canada announcement.

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The Canadian forum demonstrates a broader resilience model: instead of focusing on a single industry’s AI controls, it brings multiple critical sectors into a recurring coordination structure. Its announcement describes the forum’s remit and schedule, not independently evaluated security outcomes.

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What these examples show—and what they do not

Initiative Scope and participants Mechanism What the announcement establishes
CISA JCDC AI Cybersecurity Collaboration Playbook AI-related incidents and vulnerabilities; government, industry, international partners, and other stakeholders Voluntary information-sharing processes, with stated protections and CISA response actions Announced January 14, 2025; the announcement describes processes, not measured outcomes.
White House-directed AI cybersecurity clearinghouse Software vulnerabilities affecting AI and critical infrastructure; Treasury, NSA, CISA, AI industry, and critical-infrastructure operators Directed coordination of scans, discovery, validation, remediation, and patch distribution The June 2, 2026 order sets a formation directive and 30-day deadline; it does not establish operational status.
Treasury AIEOG AI cybersecurity and risk management in the U.S. financial system; financial institutions and federal and state regulators Development of practical resources on governance, data, transparency, fraud, and digital identity Treasury reported the work on February 18, 2026 and said six resources were planned for February; the announcement does not establish their release or effect.
Canada’s CCDC-SF Cross-sector cyber resilience, including AI-associated threats; government and leaders from ICT, energy, food, and transportation Recurring cross-sector coordination, with annual reporting planned The September 29, 2026 announcement describes its membership, remit, and schedule; it does not provide a common effectiveness measure.

These initiatives demonstrate several ways to divide the work: sharing information, coordinating vulnerability response, developing sector-specific resources, and convening operators across critical sectors. They do not provide a comparable measure of effectiveness, nor do they establish that partnership mechanisms by themselves prevent attacks. Their value depends on whether participants share useful information, act on validated findings, and coordinate fixes in practice.

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What makes a partnership useful

A partnership is more than a meeting or an announcement. Its practical value depends on having a defined purpose, the right participants, and a workable process for moving information into action. Relevant considerations include:

  • Clear scope: Specify whether the effort protects AI systems and their lifecycle, addresses malicious AI use, or improves AI-enabled defense.
  • Appropriate participants: Include the agencies, developers, operators, sector bodies, or researchers with relevant information and responsibility for action.
  • Defined handling rules: Explain what can be shared, how sensitive information is protected, and what recipients are expected or authorized to do with it. Protections described for one initiative should not be presumed for another.
  • Follow-through: Connect reports and findings to validation, prioritized remediation, and communication with affected organizations.
  • Evidence of results: Distinguish announced mandates and planned resources from operational activity and demonstrated outcomes.

Strong public-private partnerships are therefore a necessary part of a broad response to AI-based threats, but not a substitute for secure engineering, organizational risk management, or effective incident response. The central rationale is complementarity: government and industry can make better-informed decisions when they bring their different vantage points together.

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