Why teams switch
Copilots and MCP wrappers help AI ask questions. Fluency is designed to expose repeatable security work: tenant scope, evidence, durable memory, policy, reporting, and repeatable outcomes.
The difference
Natural-language UI / AI agent -> MCP tools -> vendor APIs or searches -> response
Useful for asking questions and retrieving platform-visible data. The call is usually transactional: ask, fetch, answer.
Natural-language UI / AI agent -> permission-aware skills and functions -> logic + database + tenant model + evidence store -> durable outcome
Designed for work that needs memory, policy, scope, snapshots, comparisons, and auditable deliverables.
Evaluation framework
Most platforms expose data and tools to AI. Fluency exposes repeatable security work. This table separates natural-language access from the ability to run tenant-scoped, auditable operations.
| Capability | Traditional SIEM + Copilot | SIEM MCP / API Wrapper | SOAR | Fluency Headless SIEM |
|---|---|---|---|---|
| Ask natural-language questions | Yes | Yes | Limited | Yes |
| Query existing SIEM/platform data | Yes | Yes | Sometimes | Yes |
| Run predefined searches | Yes | Yes | Yes | Yes |
| Execute permission-aware security skills | Partial | Limited | Playbook-specific | Yes |
| Resolve tenant/customer scope before work | Limited | Usually no | Sometimes | Yes |
| Pull third-party product configuration on demand | Usually no | Usually no | Connector-specific | Yes |
| Analyze configuration against policy | Partial | Only if data already exists | Playbook-specific | Yes |
| Store durable findings, snapshots, and evidence | Platform-dependent | Usually no | Case/playbook logs | Yes |
| Compare current vs previous state | Limited | Only if queried manually | Sometimes | Yes |
| Produce customer-ready reports on the fly | Limited | Usually no | Limited | Yes |
| Support health, billing, posture, case, signature, replay workflows | No | No | Partial | Yes |
| Work through UI, API, Claude, ChatGPT, dashboards | Limited | Partial | Limited | Yes |
| Keep deterministic policy and audit layer underneath AI | Partial | Tool-scope only | Playbook logs | Yes |
Vendor read
Based on publicly documented MCP, copilot, and AI assistant capabilities. Vendor capabilities change, so the useful question is not "who has AI?" It is "what work can AI safely perform?"
MCP Server, AI Assistant for SPL
Sentinel and Security Copilot style tools
Falcon MCP, Falcon Foundry skills
Scoped skills, functions, database-backed logic layer
Data access vs operational assessment
If configuration data is already inside a platform, AI may query it. That is different from fetching product configuration on demand, resolving tenant scope, normalizing it, comparing it to policy, storing the result, and producing a customer-ready finding.
It exposes tools and resources that already exist behind the server. It does not automatically discover O365, SentinelOne, AD, SaaS posture, billing, tenant hierarchy, or customer-specific service context.
An API can fetch a setting. A permission-aware skill knows when to fetch it, whose tenant it belongs to, which policy applies, what changed, what evidence to retain, and what finding to return.
When results are stored as snapshots, reports, cases, signatures, mappings, and dashboards, AI can compare state over time instead of answering from a single prompt.
Fluency logic layer
APIs are inputs. MCP is one interface. Fluency is the permission-aware operating layer between AI and the security estate: it calls APIs, queries data, stores state, compares results over time, applies policy, and returns auditable outcomes.
Start with one investigation, report, posture review, or service workflow. Fluency turns it into work your analysts, APIs, dashboards, and natural-language interface of choice can safely run.
