Security teams are drowning in data. They have SBOMs with thousands of components, vulnerability databases with hundreds of thousands of entries, license catalogs, dependency graphs, and policy configurations. The information is there. Finding it fast enough to be useful is the problem.
Griffin is Safeguard's AI assistant, built to make your supply chain security data accessible through natural language. Ask a question, get an answer. No query syntax. No dashboard navigation. No waiting for someone to pull a report.
What Griffin Does
Griffin sits on top of your Safeguard data -- SBOMs, vulnerability correlations, policies, health scores, everything -- and provides a conversational interface to it.
Some examples of what you can ask:
- "Which of our products are affected by CVE-2024-38816?"
- "Show me all components with Apache-2.0 licenses across the portfolio"
- "What changed in the dependency tree of product X between version 3.1 and 3.2?"
- "List all products using Log4j versions below 2.17.1"
- "What are our highest-risk open source dependencies?"
- "How many critical vulnerabilities were introduced in the last 30 days?"
- "Which vendor products have not provided updated SBOMs in 6 months?"
Griffin interprets these questions, translates them into the appropriate queries against your Safeguard data, and returns structured answers. It is not generating generic security advice from a language model. It is querying your actual data and presenting the results.
Why Natural Language Matters for Security
The case for natural language in security tooling is not about convenience. It is about speed during incidents and accessibility across teams.
During incident response, seconds matter. When a new critical vulnerability drops and you need to determine your exposure, the fastest path to an answer wins. Griffin can answer "are we affected by CVE-XXXX" in seconds. The alternative -- navigating to the right dashboard, constructing the right query, filtering the results -- takes minutes. During a major incident, those minutes multiply across every query, every follow-up question, every cross-reference.
Across teams, security data needs to be accessible to people who are not security specialists. Product managers need to understand the risk profile of their releases. Compliance officers need to verify regulatory readiness. Executives need to understand portfolio-level risk. Griffin gives all of these stakeholders access to supply chain data without requiring them to learn the query interface or understand the data model.
For complex queries, natural language is genuinely easier than structured query construction. "Show me all products that contain any dependency with a critical CVE, where the CVE has been public for more than 30 days and a patch is available but has not been applied" is a sentence. Expressing that as a structured query requires understanding the data model, join conditions, and filter syntax.
How Griffin Works
Griffin is not a generic chatbot with your data plugged in. It is purpose-built for supply chain security queries.
The architecture has three layers:
Intent recognition identifies what you are asking about -- a specific CVE, a product, a license type, a time range, a comparison -- and maps it to the appropriate data domain in Safeguard.
Query construction translates the recognized intent into the precise API calls needed to retrieve the relevant data. This is where domain expertise is embedded. Griffin knows that "affected by CVE-2024-38816" means checking vulnerability correlations across all SBOMs. It knows that "what changed" means computing an SBOM diff. It knows that "highest-risk" means querying health scores sorted by risk.
Response synthesis takes the query results and presents them in a human-readable format. Tables for tabular data, summaries for overview questions, lists for enumeration queries. Griffin also adds context where relevant -- if you ask about a specific CVE, the response includes the severity, affected versions, and whether a patch exists, alongside the list of your affected products.
Conversation Context
Griffin maintains conversation context within a session. You can ask follow-up questions that reference previous answers.
For example:
You: Which products are affected by CVE-2024-38816?
Griffin: 4 products are affected: [Product A v3.1, Product B v2.0, Product C v4.5, Product D v1.2]. The CVE affects Spring Framework versions before 6.1.13. All four products include spring-webmvc 6.1.10.
You: Which of those have a release scheduled in the next two weeks?
Griffin: Product A has v3.2 scheduled for release on March 28. Product C has v4.6 scheduled for April 1. The remaining products do not have near-term releases scheduled.
You: Create a ticket template for the Product A team to update Spring Framework before the 3.2 release.
This conversational flow mirrors how security analysts actually work: start with a broad question, narrow based on the answer, then take action. Griffin supports that workflow natively instead of forcing you to start from scratch with each query.
What Griffin Is Not
Griffin is not a replacement for your security team. It does not make decisions autonomously. It does not automatically remediate vulnerabilities or change your policies. It is an analyst tool -- it makes your human analysts faster and more effective by eliminating the friction between having a question and getting an answer.
Griffin also does not generate speculative security advice. Every answer is grounded in your actual Safeguard data. If you ask a question that cannot be answered from your data, Griffin will tell you what information is missing rather than guessing.
This distinction matters because trust is critical for security tooling. If an AI assistant occasionally makes up plausible-sounding but incorrect answers, it is worse than useless. Griffin's answers are traceable to specific data in your Safeguard instance, and it shows you the data alongside the interpretation.
Integration Points
Griffin is accessible through multiple interfaces:
- Web interface -- A chat panel in the Safeguard dashboard
- API -- Programmatic access for building custom interfaces or integrating with other tools
- Slack integration -- Ask Griffin questions directly in Slack channels
- CLI -- The Safeguard CLI includes a
querycommand that routes to Griffin
The Slack integration is particularly popular for incident response. When a new CVE is being discussed in your security channel, anyone can query Griffin directly in that channel. The response is visible to everyone in the conversation, which speeds up coordination.
Privacy and Data Handling
Griffin operates entirely within your Safeguard tenant. Your data is not used to train models, is not shared with other tenants, and is not sent to third-party AI services for processing. Queries and responses are logged for audit purposes within your tenant.
The AI models that power Griffin are hosted by Safeguard. We do not use generic cloud AI APIs that would expose your supply chain data to third-party providers. This is a non-negotiable requirement for enterprise customers, and we designed the architecture accordingly from the start.
Getting Started
Griffin is available on all Safeguard plans. If you have data in Safeguard, you can start asking questions immediately. There is no configuration required -- Griffin automatically has access to all data within your tenant that your user account can access (respecting your RBAC policies).
The best way to evaluate Griffin is to use it during your next vulnerability response. When a new CVE drops, ask Griffin about your exposure instead of navigating the dashboard. Time both approaches. The difference is usually compelling enough to change the workflow permanently.