OpenAI's o1 family introduced something genuinely new to the AI landscape: models that reason before answering, allocating compute to intermediate thought rather than racing straight to output. For problems that benefit from careful deliberation—mathematics, logic puzzles, complex planning—the improvement over a standard model is substantial and measurable.
A reasonable person might look at this and conclude that better reasoning would solve security. It would not. Security is a reasoning problem, but only in part. The rest of security work is grounding, policy, integration, and audit—the kinds of things no amount of reasoning compute can solve on its own.
This post walks through what deep reasoning models like o1 bring to security, where they fall short, and how Griffin AI—built on top of frontier reasoning from Anthropic's Claude family—composes deep reasoning with the engine around it.
What deep reasoning is actually good for
Deep reasoning shines when the problem has a well-defined search space and the answer can be verified once found. Competitive math, theorem proving, multi-step planning, and certain classes of software engineering challenges all fit this shape. The model spends tokens exploring candidate lines of thought, pruning dead ends, and arriving at an answer that is often dramatically better than a one-shot response.
In security work, there are real subproblems that fit this shape. Attack graph analysis—"given these privileges, these services, and these known CVEs, what is the shortest path to domain compromise?"—is a genuine reasoning task. Designing a detection rule that catches a family of behaviors without false positives is a reasoning task. Drafting a zero-trust policy that satisfies a set of constraints without blocking legitimate traffic is a reasoning task. A deep reasoning model can add real value in all of these.
So if deep reasoning is so useful, why is it not the whole answer?
Reasoning on top of what?
The honest limitation of a pure reasoning model is that it can only reason about what it sees. Give it an attack graph, it will analyze the attack graph. Give it a CVE record and a snippet of code, it will reason about that combination. But the model does not, on its own, know which attack graph applies to your organization, which code snippet is the production version, which CVE records are actually relevant given your VEX statements, or which analysis the policy requires before shipping.
Put more bluntly: a deep reasoning model is a powerful mechanism for turning good input into good output. It has no opinion on where the input comes from. In security, the provenance of the input is often more important than the quality of the reasoning over it.
This is the reason Griffin spends as much engineering effort on grounding as it does on reasoning. The frontier model we use—Anthropic's Claude family—is perfectly capable of deep reasoning when the task warrants it. What we add is the context that makes that reasoning about the right thing.
A concrete comparison
Consider a realistic security reasoning task: determining whether a critical vulnerability in a widely used library represents a real risk to a specific production service.
Handed this task with only a CVE record and a link to the repository, a deep reasoning model will do impressive work. It will read the CVE, inspect the code, trace call paths, consider the exploitability, and arrive at a thoughtful answer. The answer will be generally correct about the vulnerability's nature and incorrect about your specific exposure, because the model has no way to know your exposure.
Handed the same task, Griffin first fetches the current SBOM for the service, resolves the dependency graph including transitive relationships, pulls any VEX statements covering the advisory, retrieves the active policy, and checks whether the vulnerable symbol is reachable from the service's entrypoints. All of this happens deterministically, through tool calls scoped to the tenant. Only then does the frontier model engage its reasoning, weighing the evidence and producing a conclusion.
The reasoning component might be the same. The conclusion is dramatically different, because the input is different.
The cost structure of deep reasoning
There is a practical dimension worth mentioning. Deep reasoning models spend substantial compute on the thinking phase. For one-off hard problems, that cost is well justified. For a security workflow that processes thousands of findings a week, running every finding through deep reasoning is both expensive and unnecessary. Most findings are not deep reasoning problems—they are grounded retrieval problems with a light reasoning component on top.
Griffin is designed around this economics. For routine findings, lightweight reasoning with strong grounding handles the work efficiently. For genuinely hard questions—attack chain analysis, complex remediation planning, novel threat scenarios—Griffin escalates to deeper reasoning, because that is where the compute is worth spending. The orchestration is the point. A pure deep-reasoning workflow would burn budget on trivial cases and still lack grounding on the hard ones.
Where Griffin and deep reasoning compose
There is a version of the future where deep reasoning models and engines like Griffin compose cleanly. Griffin handles the grounding, policy, tool access, and audit. The reasoning layer, whether o1-lineage or Claude-lineage, does the thinking. For the hardest security questions—novel attack chains, ambiguous evidence, high-stakes tradeoffs—deep reasoning is genuinely helpful, and Griffin is designed to use it when it helps.
What Griffin does not do is hand off the whole workflow to a reasoning model and hope the grounding sorts itself out. Grounding does not sort itself out. It has to be engineered.
Where pure o1-style workflows struggle
The failure modes of a pure deep-reasoning workflow for security look like this. The model reasons confidently over a partial picture of reality. It produces beautifully structured analysis that reaches a conclusion the organization cannot act on, because the analysis is not connected to the systems of record. It cites nothing verifiable. It cannot be audited. It cannot be escalated to an engineer with a Jira ticket or a Slack message. It lives in a chat window and dies there.
A security program cannot be run out of a chat window, no matter how thoughtful the reasoning inside the window is. This is the core reason Griffin exists as a system around the model rather than as a wrapper on top of one.
What to actually evaluate
If you are assessing a deep reasoning model for security work, the useful experiment is to give it a realistic, multi-step task and watch where the reasoning has to guess. Does the model know which service the question is about? Does it know the current dependency versions? Does it know the active policies? Does it know who is asking and what they are permitted to see?
If the answer to any of these is "the model assumes," then the output is an educated guess dressed in confident language. Educated guesses have their place in security, but they are not a program.
The pragmatic view
Deep reasoning is a real capability, and the o1 lineage represents a real step forward in what models can do with hard problems. Griffin uses deep reasoning where it matters—complex remediation planning, attack chain analysis, policy synthesis—because the quality genuinely helps. But the bulk of security work is not solved by more thinking; it is solved by the model thinking about the right input.
That is the engine's job. That is what Griffin builds. And that is why the comparison is not "Griffin versus o1" but rather "Griffin, built on frontier reasoning, versus reasoning alone."