GPT-4o is a genuinely capable general-purpose model. It reads long documents, holds multi-turn conversations, handles images, and is cheap enough to deploy at scale. A lot of security teams started their AI journey with it, usually through a chat window or a thin wrapper, and discovered something interesting: the model is excellent at general security reasoning and quietly wrong about almost everything specific to the organization using it.
This post is about why that happens and what Griffin AI does differently. As with the rest of this series, the framing matters: Griffin is not a general-purpose model replacement. Griffin uses frontier reasoning from Anthropic's Claude family as its core, and wraps it with grounding, policy, and workflow infrastructure. The comparison below is between a pure GPT-4o workflow and a Griffin workflow, not between models.
Where GPT-4o is actually strong
Credit where it is due. For general security conversations, GPT-4o is excellent. It can explain a CVSS vector. It can describe the mechanics of a CSRF attack, or the difference between SAST and DAST, or why PKCE matters for OAuth flows. It can read a block of code and point out a likely injection flaw. Security training programs use it for exactly this kind of content, and it performs well.
None of this is in dispute. The question is whether the same model, used in the same way, can do production security work.
The limits start where the organization starts
The failure modes begin the moment the question becomes specific to your environment. Consider the kinds of questions security teams actually ask throughout a normal day:
- "Is CVE-2025-12345 exploitable in our auth service?"
- "Which of our third-party suppliers have a published VEX statement covering this week's advisories?"
- "Does the new container image in staging violate our base image policy?"
- "What is our exposure to packages maintained by sanctioned jurisdictions?"
- "Which developers have write access to projects with critical unpatched findings?"
A general-purpose model cannot answer any of these on its own. Not because the reasoning is beyond it—the reasoning is trivial—but because the data is nowhere in the model's context. The engineer is then forced to do one of two things: paste the data in by hand, which is labor-intensive and unauditable, or give up on the specific question and settle for generic advice.
Both outcomes are bad for different reasons. Pasting data into a general chat endpoint routes sensitive organizational context through a vendor boundary the security team did not design. Settling for generic advice is not security work; it is reading.
A common failure mode: confident generality
The most dangerous failure mode with a general-purpose model is not refusal. It is confident generality. Ask GPT-4o, "How should we handle CVE-2025-12345 in our checkout service?" and you will get a fluent, professional-sounding response describing the typical remediation pattern for that class of vulnerability. It will sound like advice.
It is not advice. It is a plausible-sounding average of what security practitioners typically do for that class of CVE, unconditioned on your SBOM, your reachability analysis, your policy, your team's capacity, or your SLA. The engineer who treats this as advice has accepted a strong recommendation grounded in nothing.
Griffin's answer to the same question begins with the grounded facts. The SBOM shows version 2.1.3 of the affected library. Reachability analysis shows the vulnerable function is not callable from any exposed entrypoint. The upstream vendor has published a VEX statement declaring the issue not affected for this configuration. The policy says a non-reachable, VEX-covered finding at this severity requires documentation, not remediation. The recommendation is therefore: document, close, move on.
That answer takes two seconds to produce when the system knows the data. It takes forever when the model is guessing.
What grounding actually looks like
When security teams ask how Griffin avoids the "confident generality" failure, the honest answer is that it is engineering, not prompting. Every question that arrives at Griffin is enriched with scoped retrieval before the model sees it:
- The SBOMs belonging to the project or product in scope.
- The dependency graph for the repository referenced in the question.
- Known findings, their statuses, and their triage history.
- Applicable policies, framed as constraints the model must respect.
- Integration signals—which issue tracker holds the active ticket, which CI gate is pending, which Slack channel is the right notification target.
The frontier model then reasons over this grounded context. Because the context is real, the reasoning is real. Because the context is sourced from systems the security team already trusts, the answer is auditable. Each claim in the output can be traced back to a specific SBOM record, a specific policy clause, a specific finding.
The privacy and data-boundary question
This is the other place where a pure GPT-4o workflow runs into trouble. Security teams are accustomed to thinking carefully about where their data lives. A workflow that has engineers pasting CVE details, SBOM fragments, or source code into a public chat endpoint is a data-handling decision that was not deliberately made.
Griffin runs inside the tenant boundary that security teams already govern. Frontier model calls happen under a vendor agreement that is signed, scoped, and audited. No piece of tenant data crosses a boundary the security team did not approve. This is not a capability of the model; it is a property of the system around the model.
Where frontier reasoning still carries the weight
It is worth being clear that Griffin is not replacing the intelligence of a model like GPT-4o. Griffin leans on frontier reasoning for exactly the things models are great at: summarizing, weighing tradeoffs, explaining outputs, drafting concise text for developers. The difference is that the frontier model we use—Anthropic's Claude family—does its reasoning inside an engine that knows the organization.
You could think of GPT-4o as a phenomenal general practitioner. It will identify the common cases, offer reasonable starting advice, and help you get oriented. Griffin is more like the specialist who has access to your full chart, knows your allergies, has read your imaging, and works within a medical records system that tracks every recommendation. Both are useful. They are not interchangeable.
What security teams should actually evaluate
The question to ask when evaluating any AI tool for security work is not "how smart is the model?" It is "how grounded is the system?" Smart models hallucinate. Grounded systems do not—or when they do, they tell you they are unsure, and they cite their evidence.
A practical evaluation looks like three tests. First, ask the tool a question that requires data it does not have access to. A general-purpose model will answer anyway; a grounded system will refuse or ask for the data. Second, ask the tool to cite its evidence. A general-purpose model will produce something that looks like a citation and is not; a grounded system will link to a real record. Third, ask the tool to operate under a constraint—a policy, an SLA, a data-handling rule. A general-purpose model does not know the constraint; a grounded system does.
GPT-4o passes none of these tests on its own, not because it is a bad model, but because it is a general model asked to do specialized work. Griffin passes them because the specialization lives in the engine.
The takeaway is not that GPT-4o is bad. It is that security is not general.