BriteAI is a natural-language assistant embedded directly inside TENET that lets security and operations teams ask questions about their Azure and Microsoft 365 environments in plain language and get answers grounded in live data. No query syntax. No dashboard hopping. Ask what you need to know and get a precise, contextual answer.
The problem: too much data, too little clarity
The Microsoft ecosystem generates an enormous volume of signals: metrics, logs, events, traces, security posture status, and more, spread across dozens of services and resources. When something goes wrong, finding the answer means knowing which blade to open, which query to write, and which signals to correlate.
Most teams solve this with dashboards, alert rules, and workbooks. It works until it does not, until the engineer who knows the KQL query is on holiday, until the root cause is buried three clicks deep in a view nobody checks, until two unrelated anomalies turn out to share a root cause that only becomes visible when you look at both at the same time.
The real barrier is not access to data. It is the translation layer between a question in someone's head and the answer buried in their environment.
What if you could just ask?
That is the premise behind BriteAI. Instead of navigating dashboards and constructing queries, you describe what you want to know:
"Which NSGs allow SSH or RDP from any source?"
"Why did our risk exposure jump overnight?"
"Which privileged accounts are missing MFA?"
BriteAI reasons over your live Azure and Microsoft 365 data and returns a structured, contextual answer, not a generic explanation from documentation, but a response grounded in your actual environment state.
The AI that knows where you are
BriteAI does not operate in a vacuum. Before every response, it injects live context: the tenant you are connected to, the tab you are viewing, and the current risk score. A question like "what's going on?" gets a different answer on the IAM view than on the compliance dashboard, without you having to specify which.
This is what makes BriteAI feel like a teammate watching the same screen, rather than a disconnected assistant that needs to be caught up every time.
From questions to root causes
BriteAI does not stop at describing the symptom, it pulls in data from the correlation engine to identify the probable root cause.
A practical example: your environment shows a CPU breach on vm-app-prod at 94% and an egress surge on storageacct-prod at 340% above baseline. Viewed separately, these look like two unrelated alerts. BriteAI surfaces the connection: both spiked at 03:14 UTC, and the storage account is the storage target for the VM's App layer. The CPU spike is a consequence of the egress surge and possible breach, not an independent failure. One root cause, one fix.
Remediation that goes beyond suggestions
When BriteAI surfaces an issue, it provides actionable remediation guidance: not "review your NSG rules" but "restrict inbound SSH on vm-app-prod to your corporate CIDR range and change authentication from shared key access to Managed Identity on storageacct-prod," letting you track a risk from detection to resolution in one place.
BriteAI guardrails
In a real system, guardrails are not a single filter you add at the end. They are multiple controls applied across the request path, from the API entry point to output validation. Each layer removes a different class of risk.
- Input guardrails: prompts are screened for jailbreak and prompt-injection attempts before they reach the model.
- Context construction (RAG): if retrieval is used, only approved data sources are fetched and filtered.
- Policy enforcement: policies shape what the model can access, which tools it may call, and the model generates a response within these constraints.
- Output guardrails: the response is checked for safety, supported claims, sensitive data, and compliance before returning to the user.
Want to see BriteAI in action? Get started with a 14-day free trial or request an assessment.