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Frontier AI is becoming useful for cybersecurity work such as finding software vulnerabilities, while AI-enabled workflows can also change the pace and scale of cyber operations. Organizations therefore need defense that repeats and adapts—not a one-time security project or a single safeguard. “Third era” is a useful framing for this shift, not a formally established historical period.
What changes when frontier AI enters cybersecurity?
AI can support defenders by helping identify weaknesses, but the same broad capabilities create dual-use risks. Anthropic describes practical vulnerability-discovery work and argues that defenders should adopt and experiment with AI. That is the company’s account of its work, not an independent evaluation of every model or a guarantee that AI will find vulnerabilities reliably. Anthropic’s October 3, 2025 account is best read with that distinction in mind.
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The practical implication is not that attacks are inevitable or that AI-based defenses will prevent them. It is that security teams must keep reassessing how systems are exposed, how AI is being used in their environments, and whether existing controls still fit changing capabilities and threats.
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Here, “third era” describes a shift toward cybersecurity shaped by capable AI systems and recurring adaptation. The cited sources do not establish a universally accepted three-era timeline, so the phrase should not be mistaken for a formal NIST classification or a settled historical consensus.
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A more concrete organizing framework appears in NIST’s Cybersecurity Framework Profile for Artificial Intelligence, NIST IR 8596 iprd. The December 2025 document is labeled an initial preliminary draft; it groups the work into three areas:
| Area | What organizations need to consider |
|---|---|
| Secure AI systems | Protect the AI systems themselves, including the infrastructure and access paths they depend on. |
| Use AI for cybersecurity defense | Evaluate where AI capabilities can help security teams and whether they are mature enough for the organization’s needs. |
| Thwart AI-enabled cyberattacks | Account for how AI may change cyber threats and adapt defensive controls and response practices. |
NIST’s draft cautions that “Using AI for cybersecurity defense is a dynamic area and organizations will need to continuously evaluate whether capabilities are sufficiently mature for their needs.” Because this is preliminary draft guidance, it is an organizing reference—not a final requirement or evidence that a particular implementation works.
NIST summarized the draft in its December 16, 2025 announcement. The cited materials establish the draft’s status at that time; they do not establish whether a later version has been issued.
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How does continuous defense work in practice?
Continuous defense is a recurring discover-to-verify workflow. It connects vulnerability findings to accountable owners and confirmed fixes rather than treating a scan or security sprint as the finish line.
- Inventory: Maintain a current view of code, systems, dependencies, and the business context needed to judge their importance.
- Discover: Use appropriate automated and human methods to identify candidate weaknesses, including AI-assisted methods where they fit the task.
- Validate: Check whether a finding is real, exploitable, and relevant to the organization’s systems and business risks.
- Assign: Give each confirmed issue an owner and a path to resolution.
- Remediate: Fix or mitigate the issue through the organization’s change and risk-management processes.
- Verify: Confirm that the fix works, then feed the result into monitoring and future threat assessments.
OpenAI’s Defense Factory describes this kind of cycle as a vendor reference architecture. OpenAI also reports that more than 250 people mobilized for a security sprint. That is a company-reported count for its own sprint, not a sector-wide benchmark or proof of a particular security outcome.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Why is one safeguard not enough?
The risk surface spans AI access, infrastructure, user activity, and changing threat behavior. OpenAI says its approach combines access controls, infrastructure hardening, egress controls, monitoring, detection and response, threat intelligence, and insider-risk programs, with protections refined as capabilities and threats change. These are the company’s stated practices, not a universal prescription or proof that the same set of controls is sufficient for every organization.
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OpenAI’s December 10, 2025 article on cyber resilience states: “Cybersecurity touches almost every field, which means we cannot rely on any single category of safeguards—such as restricting knowledge or using vetted access alone—but instead need a defense-in-depth approach that balances risk and empowers users.” The operational lesson is to combine controls that address different failure paths, and to keep checking whether those controls remain effective as systems and usage change.
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How should an organization assess its approach?
There is no fair product ranking or universal implementation established by the cited sources. Instead, use the frameworks as questions for reviewing coverage and operating practice:
- Does the program address securing AI systems, using AI defensively, and mitigating AI-enabled attacks?
- Does it carry findings through discovery, validation, ownership, remediation, and verification?
- Are AI-assisted actions authorized and subject to appropriate human oversight?
- Can the workflow integrate with existing security operations and escalation paths?
- Are performance and safety claims supported by independent evaluation, or only by a vendor’s description of its own work?
OpenAI’s April 15, 2025 update to its Preparedness Framework is relevant context for how a model developer describes preparedness as capabilities advance. It is a company framework, not independent evidence that a customer’s security controls will succeed.
What can the evidence support?
The cited material supports the view that AI is becoming relevant to vulnerability discovery and cyber defense, that AI-enabled threats deserve consideration, and that organizations can usefully structure their work around recurring processes and layered controls. It does not establish how much continuous AI defense reduces losses, prove comparative product performance, or show that a specific control set guarantees prevention. Vendor descriptions should remain attributed to their authors, and NIST IR 8596 iprd should be treated as preliminary draft guidance.
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