Inflection's Pi is unusual in the frontier-model landscape: it was explicitly designed as a personal, empathetic conversational assistant, and the Inflection-2.5 class model underneath is genuinely strong. For security leaders curious whether a thoughtful, conversational model could support their teams, Pi is an appealing starting point. Griffin AI, by contrast, is a security-native assistant built into the Safeguard platform. This post compares the two for the real work that security practitioners do, and where each fits.
Different design intents
Pi is positioned as an emotionally intelligent conversational partner. It excels at long, patient dialogue, coaching, and reflection. The team at Inflection has leaned into guardrails that prefer safety and warmth over raw aggression. Under the hood it is a capable model, but the product shape is deliberate: it is a companion, not an operator.
Griffin is an operator. It is built for security teams and sits on top of a live security graph. Every question it answers is backed by artifacts in Safeguard: SBOMs, vulnerabilities, findings, assets, policies, compliance evidence, and integration state. It will hold a conversation, but the conversation's purpose is to drive action and produce defensible answers.
The "thinking partner" use case
Security teams do benefit from a thinking partner. Engineers explain decisions better when they talk them through. Pi is great at this particular mode. You can describe a tricky architectural choice, ask for critiques, and get back a calm, curious response that surfaces things you had not considered. For coaching, reflection, and leadership development, Pi is a pleasant tool.
Griffin can play this role for security-specific reasoning because it has context you cannot paste into Pi. It can say "based on your production deployments, this design would pull in a dependency that currently has three critical findings open." The thinking partner mode in Griffin is informed by your ground truth.
Answering environment-specific questions
Pi has no view into your environment. If you ask "which of my services are running log4j," it cannot meaningfully answer. It can talk about log4j, explain the Log4Shell class of issues, and recommend general hygiene. That is useful but it is not what your team needs when a new CVE drops.
Griffin answers environment-specific questions by design. It walks the dependency graph, matches versions, and returns the actual list with severity, reachability, and recommended upgrade paths. The delta between "I can explain the general issue" and "I can tell you exactly which of your systems are affected" is the whole point of grounded security assistance.
Incident response
During a live incident, Pi is not the right tool. It is conversational and calm, but it cannot pull logs, cannot open tickets, cannot evaluate policies, and cannot trigger workflows. You would be tabbing between Pi and half a dozen consoles. The latency and cognitive load defeat the purpose.
Griffin is built for incident response pace. It retrieves incident-relevant artifacts, assigns tasks, opens Jira and ServiceNow tickets, posts to Slack or Teams, evaluates policy gates, and records every action for the post-incident review. It is not just chatting; it is acting on your behalf with clear guardrails.
Policy and compliance work
Security teams produce policy documents and compliance artifacts all the time. Pi can help draft text, but it has no knowledge of your control mappings, your prior attestations, or your evidence library.
Griffin has all of that. It can generate compliance reports against SOC 2, NIST, and PCI-DSS frameworks, spot evidence gaps, schedule recurring report runs, and produce customer-ready documents directly. The drafting is a small slice of the real work; Griffin owns the full workflow.
Dealing with sensitive context
Pi is a consumer-grade product in origin, and while Inflection has enterprise offerings, the product is not built around sensitive customer data isolation as its primary concern. Security teams deal with PII, source code, internal network topology, and incident details. Pasting any of that into a consumer-oriented assistant is not acceptable in most organizations.
Griffin lives inside your tenant boundary. The evidence it references is the evidence you already sent Safeguard. There is no additional data exfiltration risk, and every query is governed by policy, guardrails, and audit logging. For regulated environments, this is decisive.
Tone and cognitive load
Pi's tone is its hallmark. It is warm and patient, which matters for some kinds of work. Security engineers often appreciate terse, precise answers when they are under pressure. Griffin's tone is neutral and information-dense by default, with citations and next-action suggestions. It is less a friend and more a skilled analyst. You can change Pi's tone, but you cannot make it more terse than it is designed to be without losing its core character.
Where Pi shines
Pi is a terrific tool for leadership conversations, career coaching, and reflective work. Security leaders who want a second voice for thinking through career moves, team dynamics, or personal development will enjoy Pi. It is also good for explaining unfamiliar concepts at patient length. For a CISO taking on a new domain, Pi is a gentle tutor.
Where Griffin is essential
For the operational and technical parts of security work, Griffin is the right assistant. It is not trying to be Pi; it does not need to be. It is trying to answer questions only a system with access to your security graph can answer, and to take actions only a system integrated with your workflow can take.
Using both
A reasonable configuration is to keep Pi in your personal toolbelt for the reflective, explanatory work, and to use Griffin for the operational, evidence-based work. They do not compete in the way two frontier-model APIs might. Pi is not going to tell you which of your services are exposed to a new CVE, and Griffin is not going to coach you through a difficult team conversation. Recognizing the category difference is what lets you use each well.
The pattern across assistants
This is the repeating pattern across frontier models: generalists do generalist work well, and security teams need a tool that sits on top of ground truth to do security work. Pi is one of the most humane generalists on the market. It remains a generalist. Griffin is the security-native counterpart that makes the pairing complete.
Closing thoughts
If you are evaluating whether a conversational consumer model can support your security team, the honest answer is: it can support some parts of the work, like learning and reflection, but it cannot replace a grounded security assistant. Griffin is the tool that fits where Pi cannot reach, because Griffin is built inside the system of record. Pair them, and your team gains both a warm thinking partner and a crisp operator console.