Introduction
On February 5, 2026, OpenAI introduced Trusted Access for Cyber (TAC), an identity- and trust-based access framework designed to give verified defenders more useful access to advanced cyber capabilities while preserving safeguards against misuse.[1] TAC launched alongside GPT-5.3-Codex, which OpenAI described as its most cyber-capable frontier reasoning model at the time.[2]
Since then, the program has moved quickly. OpenAI introduced Codex Security in research preview on March 6, 2026; expanded TAC on April 14 with additional access tiers and GPT-5.4-Cyber; and, most recently, announced GPT-5.5 with TAC and a limited preview of GPT-5.5-Cyber on May 7, 2026.[3][4][5]
This is not just another AI coding assistant or vulnerability scanner. TAC is a framework for controlled access to powerful dual-use cyber capabilities. Codex Security is one of the first major applications built on that foundation: an agentic application security system that uses deep repository context, threat modeling, validation, and patch generation to help teams find and fix meaningful vulnerabilities.
At stackArmor, we have been evaluating these capabilities in controlled environments, and the implications for AppSec teams are significant. In this post, we break down what TAC is, how teams can approach access, how Codex Security scans work, how it compares with tools like Snyk, and what this shift means for the future of application security and cyber defense. The use of rapidly evolving tools like OpenAI Trusted Cyber and Anthropic Mythos has huge implications for organizations mandated to follow rigorous cybersecurity risk management practices prescribed by FedRAMP, NIST RMF and DISA/DOW RMF.
OpenAI’s Trusted Cyber enables clear, actionable analysis for security teams, by integrating elements of SAST, DAST and Threat Modeling and being a true AI-enabled Threat analysis partner without the hype.
What Is Trusted Access for Cyber and Why Does It Exist?
OpenAI’s Trusted Access for Cyber is a trust-based framework for giving verified defenders enhanced access to frontier-model cyber capabilities while continuing to restrict harmful activity.[6] The core problem TAC addresses is simple but difficult: advanced cybersecurity capabilities are inherently dual-use.
The same model that can help a product security team identify an authorization bypass in its own codebase could, in the wrong context, help an attacker look for exploitable weaknesses. Historically, broad safeguards have sometimes created friction for legitimate defenders because requests such as “find vulnerabilities in this code” can be either defensive or offensive depending on authorization, context, and intent.
TAC is OpenAI’s attempt to handle that ambiguity more precisely. Instead of relying only on blanket refusals, the program combines:
Identity verification
— Establishing baseline accountability through validated user and entity profiles.
Professional or organizational trust signals
— Factoring in verified institutional alignment and domain authority.
Use-case validation
— Confirming intended research or defensive activities map strictly to authorized scopes.
Model-level safety training
— Leveraging fine-tuned safety guardrails designed to parse context rather than block outright.
Automated monitoring
— Continuous behavioral analysis and anomaly detection to catch real-time drift.
Account-level enforcement
— Hard governance rules tied to access tokens to quickly mitigate administrative misuse.
Tiered access for more specialized workflows
— Unlocking progressively complex environments as trust structures deepen.
OpenAI has described this approach as a way to reduce friction for defenders while still preventing prohibited behavior such as credential theft, malware deployment, data exfiltration, destructive activity, and unauthorized testing.[7]
The initial February 2026 TAC announcement also committed $10 million in API credits through OpenAI’s Cybersecurity Grant Program to support teams working on open-source and critical infrastructure security.[8]
From GPT-5.3-Codex to GPT-5.5-Cyber
The TAC timeline matters because the program has evolved quickly:
- February 5, 2026: OpenAI introduced TAC alongside GPT-5.3-Codex, its most cyber-capable frontier reasoning model at the time.[9]
- March 6, 2026: OpenAI introduced Codex Security in research preview as an application security agent built to identify, validate, and help fix vulnerabilities with deep project context.[10]
- April 14, 2026: OpenAI expanded TAC to thousands of verified individual defenders and hundreds of teams, introduced additional access tiers, and launched GPT-5.4-Cyber for more permissive, specialized defensive workflows such as binary reverse engineering.[11]
- May 7, 2026: OpenAI announced GPT-5.5 with TAC as the recommended starting point for most verified defenders and introduced GPT-5.5-Cyber in limited preview for specialized workflows involving critical infrastructure and higher-risk authorized security work.[12]
The May 7 update is especially important. OpenAI now positions GPT-5.5 with TAC as the broadly most useful option for defensive security teams, including secure code review, vulnerability triage, malware analysis, detection engineering, and patch validation. GPT-5.5-Cyber is more specialized and more tightly controlled, intended for workflows such as authorized red teaming, penetration testing, and controlled validation where more permissive behavior may be necessary.[13]
This distinction is critical for AppSec leaders. Most teams should not think of TAC as a single “unlock everything” program. It is better understood as a graduated access model where safeguards, verification, and account controls increase as workflows become more sensitive.
Streamlining Vulnerability Management for Regulated Environments
stackArmor’s engineers and architects are constantly seeking ways to support customers’ ability to streamline the operationalization of controls typically associated with regulated markets. The OpenAI Trust Cyber offering helps meet specific security requirements as detailed in the table below.
| TAC Use Case | 800-53 Control Family | Control ID | Control Name | Likely Evidence / Artifacts |
|---|---|---|---|---|
| Configuration Security | Configuration Management (CM) | CM-6 | Configuration Settings | Misconfiguration findings; recommended baseline changes; drift notes |
| Incident Response / Blue Team | Incident Response (IR) | IR-4 | Incident Handling | Incident analysis notes; timeline hypotheses; containment checklist; remediation recommendations |
| Threat Modeling | Risk Assessment (RA) | RA-3 | Risk Assessment | Threat models; attack trees; risk narratives; control-gap list; prioritized mitigation backlog |
| Vulnerability Management | Risk Assessment (RA) | RA-5 | Vulnerability Monitoring and Scanning | Triage notes; severity rationale; remediation prioritization; vulnerability-to-asset mapping |
| Red Teaming / Pen Test Support | Security Assessment & Authorization (CA) | CA-8 | Penetration Testing | Red-team test plan inputs; attack scenarios; assumptions list; detection validation checklist |
| Continuous Monitoring / Purple Teaming | Security Assessment & Authorization (CA) | CA-7 | Continuous Monitoring | ConMon analysis notes; prioritized POA&M candidates; drift findings; recurring control evidence summaries |
| Monitoring / Detection Engineering | System & Information Integrity (SI) | SI-4 | System Monitoring | Detection ideas; alert tuning notes; monitoring-gap analysis; mapped ATT&CK techniques |
| Flaw Remediation | System & Information Integrity (SI) | SI-2 | Flaw Remediation | Fix recommendations; secure code examples; retest checklist; POA&M support notes |
| Security Advisories / Threat Intel | System & Information Integrity (SI) | SI-5 | Security Alerts, Advisories, and Directives | Advisory summaries; exposure analysis; affected-asset notes; action plan |
| Threat Modeling | System & Services Acquisition (SA) | SA-8 | Security and Privacy Engineering Principles | Security design review notes; design weaknesses; recommended architectural controls |
| Threat Modeling | System & Services Acquisition (SA) | SA-11 | Developer Testing and Evaluation | Code review findings; suggested test cases; remediation pull-request notes; weakness-to-CWE mapping |
What We Recommend at stackArmor
Before applying for TAC, teams should prepare the same way they would prepare for any powerful security tool rollout:
- Document specific defensive workflows. Examples include secure code review for critical services, pre-release threat modeling, validation of suspected vulnerabilities in internal systems, malware analysis in controlled environments, or patch validation for high-risk components.
- Define authorization boundaries. Make it clear which systems, repositories, environments, and assets are in scope.
- Brief legal, compliance, and security leadership. TAC can reduce friction for legitimate dual-use workflows, but it does not remove the need for internal governance.
- Start with non-production repositories. Use early scans to calibrate expectations, tune threat models, and understand the quality of findings.
- Establish review procedures. Treat AI-generated findings and patches as security-relevant recommendations that still require human review.
- Plan for account security. If your team will use advanced TAC tiers, ensure phishing-resistant authentication and strong access controls are in place.
The teams that will benefit most are the ones that approach TAC as part of a structured AppSec program, not as a standalone experiment.
How Codex Security Scans Actually Work
Codex Security, introduced in research preview on March 6, 2026, is one of the clearest examples of how TAC-enabled capabilities can change AppSec workflows.[14]
It is not a traditional static analyzer. It is an agentic application security agent that uses repository context, threat modeling, validation, and patch generation to identify higher-confidence vulnerabilities and reduce triage burden.
OpenAI describes the workflow in three major stages.
1. Repository Ingestion and Threat Model Generation
After a scan is configured, Codex Security analyzes the repository to understand the security-relevant structure of the system. It builds context around what the system does, what it trusts, where it is exposed, and how components interact.[15]
It then generates an editable, project-specific threat model. This threat model can capture:
- Trust boundaries
- Attack surfaces
- Sensitive data flows
- External integrations
- Authentication and authorization assumptions
- High-risk components
- Areas where exploitation would have meaningful impact
This is important because most vulnerability scanners operate from generic rules or known dependency patterns. Codex Security attempts to reason about the actual system.
For AppSec teams, the threat model may be as valuable as the findings. It can become a living artifact for architecture reviews, secure design discussions, onboarding, and recurring security assessments.
2. Finding, Prioritizing, and Validating Issues
Using the threat model as context, Codex Security searches for vulnerabilities and categorizes them based on expected real-world impact.[16]
This is where the system differs most from traditional scanner workflows. Instead of simply flagging low-level patterns, Codex Security attempts to identify issues that matter in the context of the application. These may include:
- Business logic flaws
- Authorization bypasses
- Cross-tenant access issues
- Unsafe trust assumptions
- SSRF paths
- Path traversal
- Session management weaknesses
- Dangerous configuration or integration patterns
- Vulnerabilities that only become meaningful across multiple components
Where possible, Codex Security also validates suspected issues in sandboxed or configured test environments. OpenAI says this validation process helps distinguish signal from noise and can enable working proof-of-concepts when an environment is tailored to the project.[17]
OpenAI reported that, during the beta, scan quality improved significantly over time. In one case, noise was reduced by 84% since initial rollout; severity over-reporting was reduced by more than 90%; and false-positive rates fell by more than 50% across repositories.[18]
That nuance matters. These numbers should not be treated as guaranteed outcomes for every codebase. They are early beta indicators. But they do suggest a meaningful shift toward lower-noise AppSec workflows.
3. Proposing Fixes With Full System Context
For validated issues, Codex Security proposes fixes that account for surrounding code and system intent.[19] This is different from a generic remediation suggestion.
A traditional scanner might say, “sanitize this input” or “upgrade this package.” Codex Security is designed to propose patches that fit the codebase, preserve intended behavior, and reduce the chance of regressions.
That does not mean teams should merge patches blindly. Security and engineering teams still need to review proposed changes, run tests, and validate business logic. But a context-aware patch can substantially reduce the time between finding and fixing a vulnerability.
4. Learning From Feedback
Codex Security can also improve from user feedback. If a team adjusts a finding’s severity, updates the threat model, or clarifies which components matter most, that feedback can help refine future scans.[20]
This is an important pattern for the future of AppSec: the scanner becomes less like a static report generator and more like a security agent that learns the system over time.
Reported Beta Performance
OpenAI reported the following results from the last 30 days of Codex Security’s beta cohort:[21]
Codex Security Report
AppsSec Intelligence | Data Insights
Overall Scan Metrics
2 Million
Commits scanned across external repositories.
Vulnerability Count
Critical
Findings
High-Severity
Findings
Data Note: Critical issues in under 1% of scanned commits.
Critical Vulnerabilities Reported to Key Open-Source Projects
GnuTLS
GOGS
libssh
PHP
Chromium
and others
As of the March Announcement
For AppSec teams, the key takeaway is not just volume. It is the combination of context, validation, prioritization, and patch generation. Codex Security is trying to produce fewer but more meaningful findings.
Codex Security vs. Traditional Tools
No single tool is a silver bullet. At stackArmor, we recommend a layered approach.
Traditional tools remain highly useful for dependency scanning, container scanning, known CVEs, license risk, and developer-friendly pull request workflows. Those workflows are fast, mature, and easy to integrate into CI/CD.
Codex Security appears to be aimed at a different layer of the problem: deeper, context-sensitive application security review.
| Dimension | Traditional SCA/SAST Tools | Codex Security |
|---|---|---|
| Primary strength | Fast detection of known dependency, container, IaC, and code-pattern issues | Context-aware vulnerability discovery, validation, and patch generation |
| Best fit | Every pull request, dependency hygiene, CI/CD guardrails | Deeper AppSec review, release gates, protected branches, critical services |
| Known CVEs | Strong | Complementary |
| Custom business logic flaws | Limited, depending on rules and language support | Stronger fit because it reasons over system context |
| Threat modeling | Usually separate from scanner workflow | Built into the workflow as an editable artifact |
| Validation | Often manual or limited | Designed to validate suspected issues where possible |
| Fix generation | Often focused on version bumps or localized recommendations | Designed to propose patches with broader system context |
| Noise profile | Mature tools can be low-noise for known dependency issues, but custom code findings may still require triage | OpenAI reports improved precision, reduced false positives, and reduced severity over-reporting during beta |
| Binary analysis | Typically outside the core workflow | Available in higher TAC tiers through cyber-permissive model access |
| Governance | Mature enterprise integrations | Emerging, tied to TAC, access tiers, and account controls |
The practical answer is not traditional tools or Codex Security, it is usually a valuable add-on to provide a value-added security assistant to the developer.
A sensible layered workflow looks like this:
- Run traditional tool on every pull request. Use them for fast dependency hygiene, known CVEs, container issues, and developer feedback.
- Run Codex Security on protected branches, major releases, and critical services. Use it for deeper review where system context matters.
- Export or preserve Codex threat models. Use them in architecture reviews and recurring security assessments.
- Feed validated findings into your ticketing system. Treat Codex findings like high-signal AppSec work items, not raw scanner alerts.
- Use human review for all proposed patches. AI can accelerate remediation, but teams remain accountable for correctness, safety, and business logic.
This layered approach lets teams keep the speed of traditional tools while adding deeper reasoning where it matters most.
Where GPT-5.5 and GPT-5.5-Cyber Fit
OpenAI’s May 7 update clarified how teams should think about the newest TAC model options.[22]
For most defenders, GPT-5.5 with TAC is the recommended starting point. It is intended for the majority of legitimate defensive workflows, including:
- Secure code review
- Vulnerability triage
- Malware analysis
- Detection engineering
- Patch validation
- Security education
- Defensive proof-of-concept work in authorized environments
GPT-5.5-Cyber is more specialized. OpenAI describes it as a limited preview for defenders responsible for securing critical infrastructure and for specialized workflows that may require more permissive behavior.[23] These can include authorized red teaming, penetration testing, and controlled validation.
The important governance point is that GPT-5.5-Cyber is not simply “better” for everyone. OpenAI has said the initial preview is not intended to significantly increase capability beyond GPT-5.5; it is primarily trained to be more permissive on security-related tasks.[24]
That means most organizations should start with GPT-5.5 under TAC and only pursue GPT-5.5-Cyber if they have a clear need, mature controls, and authorized workflows that justify additional access.
Why This Matters for AppSec Teams
Trusted Access for Cyber and Codex Security represent more than incremental tooling. They point to a structural shift in how application security work may be done.
For years, AppSec programs have struggled with the same bottlenecks:
- Too much code to review
- Too many scanner findings
- Too many false positives
- Too few security engineers
- Too much context locked in senior reviewers’ heads
- Too many vulnerabilities found late in the development cycle
Agentic security systems do not eliminate those problems, but they can change the economics. If a system can build context, generate a threat model, validate findings, and propose patches, human experts can spend more time on judgment-heavy work:
- Deciding what risks matter
- Reviewing architecture
- Validating exploitability and impact
- Coordinating disclosure
- Prioritizing remediation
- Setting policy
- Improving secure development practices
This is the right direction for AppSec. The future is not a dashboard with more alerts. The future is a set of security agents that help teams understand, validate, and fix the issues that actually matter.
Risks and Caveats
Strategic Considerations
The opportunity is real, but teams should be clear-eyed about the risks.
TAC Does Not Remove Authorization Requirements
Trusted access reduces friction for legitimate defenders. It does not authorize testing of systems you do not own or have permission to assess. Organizations must still define scope, authorization, logging, and review processes.
Research Preview Means Rapid Change
Codex Security is still in research preview. Interfaces, capabilities, pricing, integrations, and access rules may change quickly. Teams should avoid building brittle processes around early assumptions.
AI Findings Still Require Human Review
Even validated findings can be wrong, incomplete, or misleading. AI-generated patches can introduce regressions or miss business constraints. Human review remains essential.
More Permissive Models Require Stronger Controls
Higher TAC tiers and cyber-permissive models create more governance responsibility. Access should be limited to trained users, scoped to authorized environments, and protected by strong authentication.
Metrics Should Be Interpreted Carefully
OpenAI’s beta metrics are promising, but they are not universal guarantees. Every codebase is different. Teams should measure performance in their own environment before making broad claims about time savings or false-positive reduction.
Recommended Adoption Plan
For teams evaluating TAC and Codex Security, we recommend a phased approach.
Recommended Implementation Roadmap
A phased approach for teams evaluating TAC and Codex Security
Phase 1
Prepare
- • Identify 2–3 defensive workflows where advanced AI assistance would be useful.
- • Choose non-production or lower-risk repositories for initial testing.
- • Define authorization boundaries.
- • Review OpenAI’s usage policies and your internal security policies.
- • Ensure account security requirements can be met.
Phase 2
Pilot
- • Run Codex Security on selected repositories.
- • Review generated threat models with engineering and security stakeholders.
- • Compare findings against existing scanner output.
- • Track false positives, true positives, severity accuracy, and remediation time.
- • Review proposed patches, but do not auto-merge them.
Phase 3
Integrate
- • Add Codex Security scans to protected branches or release workflows.
- • Feed validated findings into ticketing systems.
- • Use threat models in architecture reviews.
- • Combine Codex Security with existing SCA, SAST, container, IaC, and secrets tools.
- • Establish review criteria for AI-generated fixes.
Phase 4
Scale
- • Expand to higher-risk services.
- • Develop internal playbooks.
- • Train AppSec and engineering teams on appropriate use.
- • Consider higher TAC tiers only when justified by authorized workflows.
- • Track outcomes: triage time, remediation time, finding quality, and repeat reduction.
What Comes Next
OpenAI’s TAC program is part of a broader shift toward AI-enabled cyber defense. The May 7 GPT-5.5 update framed this as a “security flywheel”: better models help defenders find, validate, and fix vulnerabilities faster; those defensive workflows generate feedback; and that feedback helps improve future systems.[25]
OpenAI is also expanding ecosystem partnerships. Its April 16 update named organizations including Bank of America, BlackRock, BNY, Citi, Cisco, Cloudflare, CrowdStrike, Goldman Sachs, iVerify, JPMorgan Chase, Morgan Stanley, NVIDIA, Oracle, Palo Alto Networks, SpecterOps, US Bank, and Zscaler as participants or supporters in the broader TAC ecosystem.[26] OpenAI also provided GPT-5.4-Cyber access to the U.S. Center for AI Standards and Innovation (CAISI) and the UK AI Security Institute (UK AISI) for cyber-capability and safeguard evaluations.[27]
That ecosystem approach matters. AI-enabled cyber defense will not be solved by a single model, vendor, or scanner. It will require model providers, security vendors, enterprises, open-source maintainers, researchers, and government evaluators working together.
The near-term future is likely to include:
- More context-aware AppSec agents
- More automated vulnerability validation
- Better patch generation
- Stronger integration with CI/CD workflows
- More AI-assisted threat modeling
- More use of AI in open-source vulnerability discovery
- More formal governance around dual-use cyber capabilities
For defenders, the opportunity is to move faster without lowering standards.
Closing Thoughts
The window to get ahead is open now.
Whether you are an individual practitioner, part of an enterprise AppSec team, or responsible for security in critical infrastructure, TAC and Codex Security are worth evaluating. The right first step is not to hand over security decisions to an AI system. The right first step is to test these tools in controlled, authorized environments and measure whether they improve your existing workflows.
Start with clear use cases. Use non-production repositories. Review the threat models. Validate the findings. Compare results against your existing tools. Bring legal, compliance, and security leadership into the process early.
At stackArmor, we see TAC and Codex Security as early examples of where AppSec is heading: lower-noise, context-aware, continuously improving security workflows where AI handles more of the discovery and validation burden, and human experts focus on judgment, governance, and high-impact remediation.
The code we ship today defines the attack surface of tomorrow. With the right safeguards, access controls, and operational discipline, tools like TAC and Codex Security can help defenders move faster – and more securely.
Let’s build that future responsibly.
[1]OpenAI, “Introducing Trusted Access for Cyber,” February 5, 2026. https://openai.com/index/trusted-access-for-cyber/
[2]OpenAI, “Introducing Trusted Access for Cyber,” February 5, 2026. https://openai.com/index/trusted-access-for-cyber/
[3]OpenAI, “Codex Security: now in research preview,” March 6, 2026. https://openai.com/index/codex-security-now-in-research-preview/
[4]OpenAI, “Trusted access for the next era of cyber defense,” April 14, 2026. https://openai.com/index/scaling-trusted-access-for-cyber-defense/
[5]OpenAI, “Scaling Trusted Access for Cyber with GPT-5.5 and GPT-5.5-Cyber,” May 7, 2026. https://openai.com/index/gpt-5-5-with-trusted-access-for-cyber/
[6]OpenAI, “Introducing Trusted Access for Cyber,” February 5, 2026. https://openai.com/index/trusted-access-for-cyber/
[7]OpenAI, “Introducing Trusted Access for Cyber,” February 5, 2026. https://openai.com/index/trusted-access-for-cyber/
[8]OpenAI, “Introducing Trusted Access for Cyber,” February 5, 2026. https://openai.com/index/trusted-access-for-cyber/
[9]OpenAI, “Introducing Trusted Access for Cyber,” February 5, 2026. https://openai.com/index/trusted-access-for-cyber/
[10]OpenAI, “Codex Security: now in research preview,” March 6, 2026. https://openai.com/index/codex-security-now-in-research-preview/
[11]OpenAI, “Trusted access for the next era of cyber defense,” April 14, 2026. https://openai.com/index/scaling-trusted-access-for-cyber-defense/
[12]OpenAI, “Scaling Trusted Access for Cyber with GPT-5.5 and GPT-5.5-Cyber,” May 7, 2026. https://openai.com/index/gpt-5-5-with-trusted-access-for-cyber/
[13]OpenAI, “Scaling Trusted Access for Cyber with GPT-5.5 and GPT-5.5-Cyber,” May 7, 2026. https://openai.com/index/gpt-5-5-with-trusted-access-for-cyber/
[14]OpenAI, “Codex Security: now in research preview,” March 6, 2026. https://openai.com/index/codex-security-now-in-research-preview/
[15]OpenAI, “Codex Security: now in research preview,” March 6, 2026. https://openai.com/index/codex-security-now-in-research-preview/
[16]OpenAI, “Codex Security: now in research preview,” March 6, 2026. https://openai.com/index/codex-security-now-in-research-preview/
[17]OpenAI, “Codex Security: now in research preview,” March 6, 2026. https://openai.com/index/codex-security-now-in-research-preview/
[18]OpenAI, “Codex Security: now in research preview,” March 6, 2026. https://openai.com/index/codex-security-now-in-research-preview/
[19]OpenAI, “Codex Security: now in research preview,” March 6, 2026. https://openai.com/index/codex-security-now-in-research-preview/
[20]OpenAI, “Codex Security: now in research preview,” March 6, 2026. https://openai.com/index/codex-security-now-in-research-preview/
[21]OpenAI, “Codex Security: now in research preview,” March 6, 2026. https://openai.com/index/codex-security-now-in-research-preview/
[22]OpenAI, “Scaling Trusted Access for Cyber with GPT-5.5 and GPT-5.5-Cyber,” May 7, 2026. https://openai.com/index/gpt-5-5-with-trusted-access-for-cyber/
[23]OpenAI, “Scaling Trusted Access for Cyber with GPT-5.5 and GPT-5.5-Cyber,” May 7, 2026. https://openai.com/index/gpt-5-5-with-trusted-access-for-cyber/
[24]OpenAI, “Scaling Trusted Access for Cyber with GPT-5.5 and GPT-5.5-Cyber,” May 7, 2026. https://openai.com/index/gpt-5-5-with-trusted-access-for-cyber/
[25]OpenAI, “Scaling Trusted Access for Cyber with GPT-5.5 and GPT-5.5-Cyber,” May 7, 2026. https://openai.com/index/gpt-5-5-with-trusted-access-for-cyber/
[26]OpenAI, “Accelerating the cyber defense ecosystem that protects us all,” April 16, 2026. https://openai.com/index/accelerating-cyber-defense-ecosystem/
[27]OpenAI, “Accelerating the cyber defense ecosystem that protects us all,” April 16, 2026. https://openai.com/index/accelerating-cyber-defense-ecosystem/
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