OpenAI's Call for Collective Cyber Defense: Why Automated Collective Defense Must Close the Last-Mile Gap

In August 2026, OpenAI and more than 150 organizations across industry and government backed A Call for Collective Action on Cyber Defense, emphasizing the need for coordinated cybersecurity as artificial intelligence accelerates both offensive and defensive capabilities. Cyware was among the signatories, reflecting growing recognition that organizations must move beyond isolated security operations to defend against increasingly interconnected threats.

Although collective defense has existed for decades through Information Sharing and Analysis Centers (ISACs), Information Sharing and Analysis Organizations (ISAOs), and government intelligence-sharing programs, a critical operational gap remains: threat intelligence that reaches a trusted community does not always translate into protection at participating organizations.

This last-mile problem is particularly significant for resource-constrained organizations that lack the personnel, integrations, or automation required to act quickly on shared intelligence. A threat identified by one organization may reach an intelligence-sharing hub within minutes, yet take considerably longer to become a detection, investigation, or security control at another member organization.

Automated Collective Defense addresses this challenge by connecting trusted cyber threat intelligence sharing with security orchestration, automation, and coordinated response. Cyware has been a flag-bearer for this approach, advancing technologies that help security communities turn shared intelligence into operational action, while emerging AI-powered capabilities create additional opportunities to close the last-mile gap.

As OpenAI and government agencies increasingly emphasize coordinated cyber defense, the industry's next challenge is becoming clear: ensuring that intelligence discovered by one defender can help every relevant defender act.

Why Collective Defense Has Become a Cybersecurity Imperative

What is collective defense in cybersecurity?

Collective defense is a collaborative cybersecurity approach in which organizations share threat intelligence, security expertise, and defensive capabilities to strengthen their ability to detect, prevent, and respond to common cyber threats.

The approach recognizes that many threats are not unique to individual organizations because adversaries frequently reuse infrastructure, vulnerabilities, and tactics, techniques, and procedures (TTPs) across entire industries.

Consider a phishing campaign targeting several financial institutions, where one bank identifies a malicious domain, another discovers related command-and-control infrastructure, and a third uncovers a malware variant associated with the same campaign. Individually, each institution possesses only part of the threat picture, but through collective threat intelligence sharing, their findings can provide a more comprehensive understanding of the attack.

The real value emerges when that combined intelligence becomes actionable through detection rules, investigations, preventive controls, and coordinated response workflows across participating institutions. This distinction is fundamental because collective defense is not simply about exchanging information; it is about enabling organizations to use shared knowledge to improve their defensive readiness.

Why isolated security operations struggle against shared adversaries

Most Security Operations Centers (SOCs) are designed to protect individual organizations, with security telemetry, technologies, and response procedures operating primarily within their own environments.

Although this model remains essential, it creates inefficiencies when multiple organizations independently investigate the same malicious infrastructure, validate identical indicators of compromise (IOCs), and develop overlapping detection and mitigation strategies. These duplicated activities consume resources and delay defensive action, particularly for smaller organizations that lack dedicated threat intelligence teams or mature security operations capabilities.

Collective defense creates an opportunity to distribute expertise across trusted communities, reducing the need for every participant to independently repeat the same analytical work.

Why AI raises the stakes

Artificial intelligence is accelerating threat analysis, intelligence enrichment, security investigation, and defensive automation, but similar capabilities can also support malicious reconnaissance, phishing development, vulnerability research, and other offensive activities.

As AI changes the pace of cyber operations, the time available to recognize and respond to emerging threats may become increasingly constrained, making the speed of intelligence sharing and operational response more consequential.

A detection developed by one organization should therefore help other relevant organizations strengthen their defenses without requiring every participant to repeat the entire investigation.

OpenAI's Call for Collective Cyber Defense: From Shared Responsibility to Coordinated Action

OpenAI's call for collective action on cyber defense emphasizes empowering defenders with advanced capabilities, strengthening partnerships, and coordinating cybersecurity efforts across governments, technology providers, enterprises, and critical infrastructure operators.

Importantly, the initiative recognizes that organizations responsible for essential services may lack the financial resources, technical expertise, or security personnel required to deploy advanced defensive capabilities independently.

Why OpenAI's call goes beyond intelligence sharing

Effective collective defense requires more than exchanging information about emerging threats because organizations also need mechanisms to deploy defensive technologies, validate mitigations, coordinate vulnerability remediation, and verify that recommended security improvements work.

Consider a critical vulnerability affecting technology used by hundreds of organizations, where one security team identifies exploitation activity and another develops an effective mitigation. Sharing these findings is valuable, but the broader defensive benefit depends on whether affected organizations can identify their exposure, implement appropriate controls, and confirm their effectiveness.

OpenAI's call therefore reinforces a shift from collective threat awareness toward collective defensive execution, particularly for organizations that lack the resources to operationalize shared intelligence independently.

Government agencies are moving in the same direction

OpenAI's call is not an isolated development because government agencies and public-sector cybersecurity organizations are also emphasizing the need to translate shared intelligence into coordinated defensive action.

Several developments in 2026 illustrate this direction.

• CISA 2030: Making collective defense an operational priority. In its October 2026 strategy, CISA 2030: Defend, Harden, Prevail, CISA identifies collective cyber defense as a strategic objective and emphasizes integrating defensive capabilities across government, industry, state, local, tribal, and territorial partners, alongside international organizations. The strategy also highlights strengthening the intelligence-to-operations pipeline through modernized capabilities that support timely defensive decisions.

• Executive Order 14409 and Gold Eagle: Strengthening vulnerability coordination. Federal initiatives introduced between June and August 2026 highlight the importance of coordinating vulnerability-related information and defensive activities. Executive Order 14409 directed the development of an AI cybersecurity clearinghouse involving the Treasury Department, CISA, and the Office of the National Cyber Director, while the associated Gold Eagle initiative focuses on vulnerability coordination. CISA's accompanying guidance also discusses automating vulnerability prioritization where appropriate to manage increasing volumes of security information.

• State governments and NASCIO: Extending collective defense to resource-constrained organizations. The 2026 NASCIO-Deloitte Cybersecurity Study examines the expansion of whole-of-state cybersecurity programs supporting local governments, public education, and critical infrastructure organizations while highlighting persistent workforce and budget challenges. The 2026 NASCIO State CIO Survey reports that 73% of state CIOs identify critical infrastructure cybersecurity protection as part of their whole-of-state plans.

Although these initiatives differ in scope, they reflect a common operational requirement: shared cybersecurity intelligence and expertise must translate into timely defensive action, including for organizations that cannot independently maintain sophisticated security capabilities.

However, establishing trusted information-sharing networks does not automatically guarantee that intelligence reaches security controls, triggers investigations, or informs response activities across participating organizations.

The Last-Mile Problem: Why Traditional Threat Intelligence Sharing Falls Short

Cyber threat intelligence sharing has evolved considerably through trusted communities, commercial intelligence platforms, government programs, and standards such as STIX and TAXII, which support structured, machine-readable intelligence exchange.

However, successful intelligence distribution does not necessarily mean participating organizations can operationalize that intelligence effectively.

How hub-and-spoke intelligence sharing works

Many collective defense communities operate through a hub-and-spoke model, where a central organization such as an ISAC, ISAO, government agency, or state SOC collects intelligence from participating members and external sources.

The hub validates and enriches the information, applies appropriate sharing restrictions, and distributes relevant findings to member organizations, which then determine how to incorporate the intelligence into their own security operations.

This model centralizes specialized expertise and establishes trusted collaboration across organizations facing related threats, but its effectiveness depends on what happens after intelligence leaves the hub.

Where the last mile breaks down

Consider a regional electric utility receiving a validated advisory about malicious infrastructure associated with an active campaign.

Although the intelligence may be accurate and timely, the utility must still determine whether its environment is affected, extract relevant indicators, search security telemetry, assess exposed assets, develop detection logic, and implement approved mitigations.

For an organization with limited cybersecurity personnel, these activities can take considerable time, particularly when intelligence arrives through disconnected portals, emails, or reports.

Several challenges contribute to this gap:

• Manual intelligence consumption: Security teams must monitor multiple channels before identifying relevant information.

• Insufficient contextualization: Shared intelligence may lack the confidence, asset relevance, or threat context needed for operational decisions.

• Disconnected security technologies: Intelligence may not integrate directly with SIEM, EDR, XDR, firewalls, or incident management systems.

• Uneven automation maturity: Participating organizations may have different capabilities for translating intelligence into defensive workflows.

• Limited feedback visibility: Sharing communities may not know whether members received, investigated, or acted on distributed intelligence.

Consequently, a community may distribute thousands of indicators and advisories without knowing how many resulted in meaningful defensive action.

The challenge is not necessarily a failure of intelligence-sharing hubs, which often perform the difficult work of collecting, validating, and contextualizing threat information. Rather, the operational pipeline frequently breaks between the hub and the security controls of participating members.

As attackers rotate infrastructure and modify techniques, intelligence can lose operational relevance before manual processes translate it into action, making the last mile an increasingly important component of collective defense.

Automated Collective Defense: Turning Shared Intelligence Into Coordinated Action

Automated Collective Defense is a cybersecurity approach that combines threat intelligence management, trusted information sharing, security orchestration, and automation to help participating organizations translate shared intelligence into coordinated detection, investigation, and response activities.

It extends traditional collective defense by connecting intelligence exchange with operational security workflows while allowing each participant to maintain its own security policies, approval requirements, and operational controls.

How Automated Collective Defense works

An effective Automated Collective Defense model connects several complementary capabilities.

• Automated intelligence collection and enrichment: Intelligence from commercial feeds, open-source sources, government advisories, internal telemetry, and trusted communities is normalized, deduplicated, correlated, and enriched with threat context, confidence assessments, and associated infrastructure.

• Relevance-based intelligence distribution: Communities can route intelligence according to sector, technology exposure, threat type, organizational risk profile, and sharing permissions, helping participants focus on findings relevant to their environments.

• Sharing actionable defensive content: Beyond indicators such as malicious IP addresses and domains, communities can exchange adversary TTPs mapped to MITRE ATT&CK, detection content such as Sigma and YARA rules, vulnerability intelligence, validated mitigations, and response guidance.

• Security orchestration and automation: Integrations with SIEM, SOAR, EDR, XDR, firewalls, and case management systems enable intelligence to trigger predefined investigation, detection, and approved response workflows.

• Bidirectional feedback: Participating organizations can report sightings, false positives, investigation findings, and response outcomes, helping communities refine intelligence quality and improve subsequent defensive decisions.

From shared intelligence to member-level action

Consider a scenario where one organization identifies a malicious command-and-control domain associated with an active campaign.

An Automated Collective Defense workflow can validate and enrich the indicator, identify relevant community members, and distribute the intelligence through approved sharing channels. Participating organizations can then automatically search connected security tools for matching activity, create investigation cases, and initiate appropriate mitigation workflows.

Depending on local policies, high-confidence findings may trigger automated controls, while potentially disruptive actions may require analyst approval. This enables organizations with different security capabilities to participate in coordinated defense without surrendering control over their environments.

Why feedback is essential

Traditional intelligence sharing frequently follows a one-directional model where information is published and consumed without consistent visibility into what happens afterward.

Automated Collective Defense introduces feedback mechanisms through which members can report whether shared intelligence was observed, whether detections were effective, and whether additional threat infrastructure was discovered.

Structured formats such as STIX 2.1 Sighting objects can support the exchange of relevant observations, creating a continuously improving intelligence ecosystem where operational findings from one participant contribute to better decisions across the community.

How Cyware Has Been a Flag-Bearer for Automated Collective Defense

While OpenAI's call has renewed industry attention on collective cyber defense, Cyware has been advancing the technologies and operational approaches needed to make collaborative cybersecurity actionable.

As a flag-bearer for Automated Collective Defense, Cyware has focused on addressing the gap between intelligence sharing and defensive execution by connecting threat intelligence management, trusted collaboration, security orchestration, and automation.

Its Automated Collective Defense capabilities support coordinated workflows across ISACs, ISAOs, government agencies, enterprises, and critical infrastructure communities, helping participating organizations operationalize shared intelligence while maintaining their own security policies.

Connecting threat intelligence sharing with automated action

Cyware supports the aggregation, normalization, enrichment, correlation, and prioritization of intelligence from multiple sources, enabling sharing communities to develop a more consistent understanding of emerging threats.

Its trusted sharing capabilities help communities distribute relevant findings through controlled workflows that account for recipient relevance, sharing permissions, and approval requirements. Through integrations with existing security technologies, Cyware also connects shared intelligence with investigations, detection activities, case management, and approved response actions.

For intelligence-sharing hubs, this reduces the manual effort required to process and distribute actionable findings, while participating organizations benefit from intelligence that can enter their security workflows without extensive manual translation.

How Cyware AI extends last-mile actioning

Cyware is extending these capabilities through Cyware AI, which brings purpose-built AI agents into threat intelligence and collaborative security workflows.

These agents help contextualize shared intelligence against organizational priorities, assets, vulnerabilities, and exposure, reducing the analytical effort required to determine which threats demand attention.

Capabilities such as the Priority Intelligence Requirement Agent, Vulnerability Watch Agent, and Threat Briefing Agent support the translation of shared findings into member-relevant intelligence and recommended actions. Combined with security orchestration and automation, these capabilities create opportunities to move more efficiently from collective threat awareness to context-aware defensive workflows, particularly for organizations with limited analytical resources.

This is where last-mile actioning agents become relevant because they can help bridge intelligence interpretation, local relevance assessment, and authorized workflow execution across connected security technologies.

Rather than replacing established security controls or human judgment, these agents complement Automated Collective Defense by helping organizations determine what shared intelligence means for their environments and which defensive activities should follow.

Enabling trusted, bidirectional collaboration

Collective defense becomes more valuable when intelligence flows in both directions rather than exclusively from a central hub to participating members.

Cyware supports collaborative workflows through which organizations can contribute threat observations, receive validated findings, and share relevant investigation outcomes. This feedback-driven approach can improve intelligence quality, identify emerging campaigns, and strengthen the community's understanding of threats affecting its members.

Cyware's significance therefore lies not simply in facilitating intelligence exchange, but in helping security communities connect shared knowledge with operational outcomes.

Collective Defense in Practice: Space-ISAC, Electric Cooperatives, and Government Communities

The value of Automated Collective Defense becomes evident when organizations facing common threats can share intelligence and act on it, even when their security resources, technologies, and operational requirements differ. Our work with intelligence-sharing communities and critical infrastructure organizations demonstrates how this approach can help bridge the gap between collective threat awareness and coordinated defensive action.

Space-ISAC: Accelerating intelligence sharing and response

The space ecosystem brings together satellite operators, communications providers, manufacturers, and ground infrastructure operators whose security interests are closely interconnected. A threat discovered by one organization may have implications across the broader community, making timely intelligence sharing and coordinated response essential.

Through our work with Space-ISAC, we have helped strengthen intelligence-sharing and collaborative security workflows, including cross-domain threat detection, automated response playbooks, and coordinated incident response.

As detailed in our Space-ISAC case study, intelligence-sharing time improved from approximately four to eight hours to five to thirty minutes, while mean time to respond decreased from approximately four to eight hours to thirty to sixty minutes.

These results illustrate what becomes possible when intelligence sharing is connected directly to operational workflows, reducing the time between threat discovery, intelligence distribution, and defensive action.

NRECA: Advancing collective defense for electric cooperatives

We are also supporting the National Rural Electric Cooperative Association (NRECA) on U.S. Department of Energy-funded research focused on advancing Automated Collective Defense for electric cooperatives.

As outlined in our September 2026 announcement, this initiative explores how collaborative security capabilities can strengthen cyber resilience across electric cooperative environments, where distributed infrastructure, operational technology, and limited cybersecurity resources create distinctive challenges.

In these environments, automation must account for operational safety and service continuity. Intelligence about suspicious activity affecting industrial systems, for example, may appropriately trigger enhanced monitoring, investigation, or analyst review rather than immediate automated blocking.

Our involvement in this research reflects our commitment to making collective defense practical for critical infrastructure organizations, while recognizing that defensive automation must operate within appropriate safety and governance boundaries.

Whole-of-state cybersecurity: Extending protection to resource-constrained organizations

Whole-of-state cybersecurity programs provide another important opportunity to extend the benefits of collective defense to local governments, educational institutions, and other organizations that may lack dedicated threat intelligence teams or mature security operations capabilities.

In a hub-and-spoke model, a centralized state security function can collect, validate, and distribute threat intelligence while participating organizations consume relevant findings through their own security workflows.

Our approach to Automated Collective Defense is designed to support these environments by connecting trusted intelligence sharing with security orchestration, automation, and feedback mechanisms. This enables state-level cybersecurity programs to extend centralized expertise across participating organizations without requiring every member to build and maintain equivalent capabilities independently.

Across these use cases, the objective remains consistent: make shared threat intelligence actionable across organizations with different resources, technologies, and operational requirements.

What It Takes to Make Collective Defense Work at Scale

Automated Collective Defense can improve the speed and consistency of collaborative security operations, but successful implementation requires governance, interoperability, validation, and accountability.

Trust, governance, and information-sharing policies

Organizations need confidence that shared intelligence will be handled appropriately, particularly when it contains sensitive incident details, infrastructure information, or findings subject to redistribution restrictions.

Communities should establish clear policies governing data classification, access permissions, permitted use, retention, and onward sharing, with the Traffic Light Protocol (TLP 2.0) providing a common mechanism for communicating sharing restrictions.

Protecting against inaccurate or poisoned intelligence

Automation can amplify the consequences of inaccurate intelligence because incorrect findings may be distributed rapidly across multiple organizations.

An improperly classified indicator could cause legitimate infrastructure to be blocked, while deliberately manipulated intelligence could introduce misleading conclusions into shared workflows.

Communities therefore need source validation, contributor controls, confidence thresholds, enrichment processes, and appropriate approval requirements before shared intelligence triggers consequential actions.

Interoperability and responsible AI-powered cyber defense

Collective defense communities rarely operate identical security architectures, making interoperability essential for translating intelligence into workflows compatible with different SIEM, EDR, firewall, cloud security, and incident response technologies.

AI and automation can reduce repetitive analytical work, but they should operate within clearly defined authorization boundaries supported by role-based access controls, approval workflows, audit trails, testing, and rollback mechanisms.

These safeguards become increasingly important as last-mile actioning agents and other agentic AI capabilities support multi-step investigations and interact with connected security systems.

The objective is to accelerate well-defined activities while preserving human accountability for consequential decisions.

Measuring collective defense outcomes

Traditional intelligence-sharing metrics often emphasize the number of indicators distributed, reports published, or community members enrolled, but these measurements do not necessarily demonstrate improved security.

More meaningful metrics evaluate whether shared intelligence becomes operationally useful.

Among these measurements, last-mile coverage is particularly important because it helps determine whether collective defense is producing operational protection rather than simply increasing information exchange.

The Future of Collective Defense Is AI-Powered, Automated, and Collaborative

The next evolution of collective defense will involve closer integration between AI-assisted intelligence processing, security automation, and cross-organizational collaboration.

AI can help transform unstructured reports into structured intelligence, identify relationships between campaigns, prioritize findings for different communities, and recommend relevant detection or investigation workflows.

Emerging agentic AI capabilities may further reduce the manual effort required to operationalize shared intelligence, with last-mile actioning agents helping connect community-level intelligence to context-aware defensive workflows at individual organizations.

However, these capabilities also introduce risks involving inaccurate model outputs, sensitive information exposure, unauthorized actions, and incorrect recommendations propagating across trusted communities.

Effective collective defense must therefore combine AI-assisted speed with intelligence validation, interoperable technologies, appropriate authorization controls, and human oversight, ensuring that automation strengthens defensive operations without compromising accountability.

From Collective Responsibility to Collective Action

OpenAI's call for collective cyber defense, CISA's strategic direction, federal vulnerability coordination initiatives, and the expansion of whole-of-state cybersecurity programs all reinforce a common conclusion: cybersecurity must become more collaborative, operationally connected, and accessible to organizations with different levels of resources.

The industry has already established many of the foundations through ISACs, ISAOs, government partnerships, and intelligence-sharing standards, but the effectiveness of these efforts increasingly depends on what happens after intelligence is distributed.

The next stage of collective defense will be defined by how successfully organizations close the last-mile gap between shared intelligence and defensive action.

Automated Collective Defense addresses this challenge by connecting intelligence management, trusted sharing, security orchestration, automation, and feedback into coordinated workflows, while AI-powered last-mile actioning can make those workflows more context-aware and responsive to individual organizational needs.

As a flag-bearer for Automated Collective Defense, Cyware has been advancing this model through technologies and initiatives that help security communities move from shared threat awareness toward operationally actionable intelligence, with Space-ISAC and ongoing research supporting electric cooperatives illustrating its practical relevance.

Ultimately, the future of collective defense will not be measured solely by how much intelligence organizations exchange, but by how effectively that intelligence helps participating organizations detect threats, make informed decisions, and implement appropriate protections.

The goal is to make what one defender learns actionable for every defender who needs it.

Frequently Asked Questions About Collective Defense

What is collective defense in cybersecurity?

Collective defense is a cybersecurity approach in which organizations share threat intelligence, expertise, and defensive capabilities to improve their ability to detect, prevent, and respond to threats affecting multiple participants.

What is Automated Collective Defense?

Automated Collective Defense combines trusted threat intelligence sharing with security orchestration and automation, helping organizations translate shared intelligence into detection, investigation, response, and feedback workflows while maintaining control over their own security policies.

What is the last-mile problem in threat intelligence sharing?

The last-mile problem refers to the gap between intelligence being distributed by a trusted sharing community and that intelligence becoming operationally useful within participating organizations, often because of manual processes, disconnected technologies, or limited security resources.

How is Automated Collective Defense different from traditional threat intelligence sharing?

Traditional threat intelligence sharing primarily focuses on exchanging information, whereas Automated Collective Defense connects that information with security tools, operational workflows, and feedback mechanisms so participating organizations can act on relevant findings more efficiently.

How does Cyware AI support collective defense?

Cyware AI brings purpose-built agents into threat intelligence and security workflows, helping organizations contextualize shared intelligence, prioritize relevant threats, generate actionable insights, and support authorized defensive activities.

What are last-mile actioning agents?

Last-mile actioning agents are AI-powered capabilities designed to help translate threat intelligence into relevant defensive workflows by considering organizational context, recommending appropriate actions, and supporting authorized tasks across connected security tools.

Why is AI important for collective cyber defense?

AI can accelerate threat analysis, intelligence enrichment, investigation, and workflow execution, helping organizations process and operationalize shared intelligence more efficiently when appropriate validation, governance, and human oversight are maintained.