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Security teams should use AI to find and fix weaknesses in their own environments before attackers exploit them—not wait for an alert or incident to reveal what needs attention. That is the central argument of Chaim Mazal’s sponsored contribution to The New Stack. It does not mean abandoning defensive controls: official Five Eyes guidance stresses that foundational security and incident readiness still matter as AI accelerates threats.
What does “no room for a defender’s mindset” mean?
Mazal uses “defender’s mindset” to describe a security approach that is primarily reactive: teams wait for alerts, incidents or vendor patches to identify problems, then respond. His sponsored article argues for a more proactive, engineering-led approach: deliberately inspect systems and workflows for weaknesses and remediate them before an adversary finds them. He summarizes that position as: “In this environment, defending attack surfaces won’t cut it. We need to go on the offense.”
Here, “on the offense” means taking initiative in security work, not conducting unauthorized attacks. It is a change in when and how a team looks for risk—not a reason to discard access controls, patching, monitoring or incident response. The argument and quotation are Mazal’s, in an article sponsored by GitLab; they should not be mistaken for independent product testing or official Five Eyes guidance. Read the sponsored article in The New Stack.
Why does AI make proactive security more urgent?
An official statement from Five Eyes cyber security agencies says AI accelerates the speed, scale and sophistication of cyber threats, while also creating opportunities to strengthen defense. The agencies state: “Frontier AI models are anticipated to exceed current industry expectations, fundamentally transforming both offensive and defensive cyber capabilities.” The statement is dated June 22, 2026; it is a qualitative warning, not a quantified forecast of when a particular capability will arrive.
That dual-use point matters for security planning. Organizations should consider how AI may change threats without assuming that AI itself is a security control. The agencies’ statement emphasizes established measures—including reducing exposure, patching, managing identity and access, and preparing for incidents—alongside the need to make use of defensive capabilities. Read the Five Eyes statement hosted by the UK National Cyber Security Centre.
How can teams use AI to look for weaknesses?
Mazal recommends applying AI within security workflows to help examine an organization’s own environment and uncover issues sooner. The practical implication is to make the work specific enough to review: define the asset or workflow, the question to investigate, the context the system may use, and what a human must verify before any change is made. AI assistance can help direct attention; the article does not establish that an agent can independently find every vulnerability or safely remediate it.
Keep agent tasks narrow
Rather than giving an agent a vague instruction such as “secure our environment,” assign a bounded task with relevant context and an observable result. A team might ask it to review a defined configuration or change for specified policy concerns, then have a qualified person validate findings and decide on remediation. This is an illustration of Mazal’s narrow-task recommendation, not a tested procedure or product-specific capability.
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Keep people accountable for consequential changes
Teams should decide in advance which actions AI may suggest, which require approval, and how to record and reverse changes. This is especially important when a recommendation could affect access, production systems or incident containment. The Five Eyes statement separately calls for leadership accountability and confidence that controls will work during a real incident; it does not endorse a particular AI agent or vendor.
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The Five Eyes agencies’ recommendations keep AI security grounded in controls that reduce the chance and impact of compromise. Their guidance frames cyber risk as a core business risk and a leadership responsibility, not solely a technical-team concern.
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- Reduce attack surface: review exposed systems and services and remove exposure that is not needed.
- Patch faster: prioritize timely remediation, including attention to legacy systems that may be harder to update.
- Strengthen identity and access: manage who and what can access systems, and limit access to what is required.
- Prepare for incidents: plan and exercise response so teams can contain and recover when prevention fails.
These are recommendations attributed to the official Five Eyes statement, not claims that Mazal’s sponsored article independently tested their effectiveness. Together, they provide a practical counterweight to the idea that a new AI capability can substitute for security fundamentals.
What should organizations evaluate before adopting AI security workflows?
Mazal’s contribution recommends flexibility across models and vendors, and the option of air-gapped or self-hosted deployment where data residency or intellectual-property protection calls for it. These are the author’s implementation recommendations, not requirements in the Five Eyes statement. A suitable choice depends on the organization’s data, operating constraints and ability to manage the deployment.
| Decision area | What to assess | What the cited sources establish |
|---|---|---|
| Model and vendor flexibility | Whether workflows can accommodate a change of model or provider without being tied to one option. | Mazal recommends avoiding dependence on a single model or vendor; the sources do not rank providers. |
| Deployment and data handling | Whether data residency or intellectual-property concerns call for air-gapped or self-hosted deployment. | Mazal identifies these as options to consider; the sources do not establish which deployment is best for a given organization. |
| Agent scope and context | Whether each task is bounded and supplied with context relevant to the work. | Mazal recommends well-defined tasks and relevant context; the sources do not provide comparative agent test results. |
| Foundational controls | Whether exposure, patching, legacy systems, identity and access, and incident preparation are addressed. | The Five Eyes agencies call for these areas of action; the statement does not rate specific products. |
| Containment and recovery | Whether the organization can respond and recover when a control fails or an incident occurs. | The Five Eyes agencies urge incident preparation and confidence in controls under real conditions; no vendor comparison is provided. |
Neither source establishes a tested or ranked set of security products. The Five Eyes statement does not endorse GitLab or another commercial vendor; GitLab’s connection here is that it sponsored Mazal’s New Stack article.
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A practical starting point is to connect proactive discovery to the security program the organization already runs, rather than treating AI as a separate shortcut.
- Choose a defined scope. Identify the systems, configuration, change or workflow to examine, and the risk the review is meant to address.
- Set task boundaries. Give an AI agent only the context and permissions needed for that task; establish what it may recommend and what requires human approval.
- Validate findings. Have responsible staff check whether a finding is accurate, relevant and actionable before remediation.
- Fix the underlying weakness. Feed confirmed issues into patching, access management, exposure reduction or the appropriate remediation process.
- Exercise incident readiness. Check that response and recovery plans can work in practice, including when AI-assisted tools or other controls are unavailable.
This sequence is a way to operationalize the sources’ recommendations, not a prescribed standard issued by the agencies. Leaders remain responsible for deciding whether the workflow fits their organization and for ensuring core controls are maintained.
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