Sentinel Data Lake and Graph Enable Agentic Security with Avanade

Microsoft’s security stack just crossed a new practical milestone: Avanade has been named a design partner on the reinvigorated Microsoft Sentinel platform and is shipping the first Security Copilot–compatible agents that run against the newly available Sentinel data lake, while Microsoft has moved the data lake into general availability and opened public previews for a graph layer and a Model Context Protocol (MCP) server—advances that promise richer, longer‑range threat detection and faster SOC automation, but which also raise new governance, cost, and attack‑surface considerations.

Background​

The last two years have pushed SIEMs to a turning point: telemetry volumes have exploded, security teams are understaffed, and effective long‑tail forensic analysis has become prohibitively expensive when operating on indexed analytics alone. Microsoft’s response reframes Sentinel from a cloud‑native SIEM into a broader, agentic security platform: a centrally managed security data lake that stores raw and processed telemetry for extended periods, a graph that connects identities, assets and events, and an MCP server that standardizes agent access to tenant context. These elements are designed to make Security Copilot agents and partner‑built agents both more effective and interoperable.

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What changed: Sentinel data lake, graph, and MCP in practical terms​

The Sentinel data lake — purpose and architecture​

• The Sentinel data lake provides a central repository for both structured and semi‑structured security telemetry, designed to reduce duplication between analytics and storage tiers and to make long‑term retention affordable. Microsoft positions this as the foundation for retrospective hunts, model training, and agent reasoning.
• The lake stores data in open, queryable formats that can be accessed by multiple analytic engines (for example Kusto, Spark, and ML tooling), enabling both low‑latency detection queries and heavy historical analysis on the same raw dataset. This separation of storage (lake) from indexing (analytics) is what underpins the cost model and retention flexibility.
• Microsoft moved the Sentinel data lake to general availability on September 30, 2025, signaling readiness for production workloads while continuing to preview the graph and MCP server. Independent coverage and Microsoft’s own community posts confirm the GA timing.

Why this matters: storing more telemetry for longer gives AI models the data breadth needed to detect subtle patterns and to reconstruct attacker timelines—capabilities that were impractical when teams had to prune data aggressively to control index costs. At the same time, the central lake concentrates risk and requires mature access controls, cost governance, and compliance mapping.

Graph layer — making relationships first class​

• The Sentinel graph models relationships between users, devices, processes, network flows, alerts, vulnerabilities, and indicators as nodes and edges. Graph queries can surface likely attack paths and compute blast‑radius scores more directly than flat joins. This is designed to make agent reasoning and impact analysis both faster and more accurate.
• For analysts, graph context is a force multiplier: a single alert can be traversed to show downstream resources and potential lateral movement in fewer steps, improving triage and prioritization.

Model Context Protocol (MCP) server — a standardized bridge for agents​

• The MCP server is a tenant‑side runtime that exposes contextualized data and tool APIs to models and agents using a standardized protocol. By localizing the MCP server inside customer control planes, Microsoft aims to balance agent flexibility with enterprise oversight, auditability, and data residency constraints.
• MCP is intended to reduce bespoke connector work by giving agents a common interface for reading context, calling the graph, querying the data lake, and requesting actions (create tickets, quarantine devices, etc.). That standardization is central to the marketplace model Microsoft envisions for Security Copilot agents and partner agents.

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Avanade’s role and the new partner agents​

Design partnership and practical co‑development​

Avanade—the Microsoft‑Accenture joint venture—has been working with Microsoft product teams as a Sentinel design partner to ensure the product reflects real‑world SOC needs. That collaboration reportedly shaped features and operational capabilities, including telemetry cost visibility for Security Copilot usage. Avanade has publicly announced three initial agents and plans to publish them to the Microsoft Security Store.

The first Avanade agents: what they do​

• Sentinel Analytics Optimizer — reduces noise by identifying noisy incidents and optimizing rule sets, aiming to lower false positives and improve SOC analyst efficiency. This agent is positioned to act as a continuous rules‑tuning assistant.
• Threat Shield — focuses on ingesting and operationalizing threat intelligence sources, generating or tuning Sentinel detection rules to improve coverage informed by external intelligence. The design intent is to close detection gaps that static rulebooks miss.
• Endpoint Risk Insights — consumes Defender for Endpoint signals, correlates them with exploited‑vulnerability feeds, and produces prioritized lists of devices that require patching or isolation. This is meant to convert noisy vulnerability inventories into operational remediations.

Independent press reporting, industry briefs, and Avanade statements corroborate the agent names and Avanade’s design‑partner status. Note that these agents are architected to deploy through Security Copilot workspaces and to leverage the Sentinel data lake and MCP runtime as they mature.

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How the pieces fit operationally: from telemetry to automated action​

Ingestion and connectors​

• Microsoft asserts the same connectors that feed Sentinel’s analytics tier can route data to the lake, preserving existing integration investments while enabling lake‑scale retention. That universal connector architecture is a critical enabler for multi‑vendor data consolidation (for example, SAP logs, third‑party endpoint telemetry, cloud provider logs).
• Partners and third‑party vendors can build MCP‑compatible agents or publish Security Copilot packages to the Security Store, enabling managed distribution and standardized onboarding. This marketplace approach reduces integration friction but also raises vendor‑validation and supply‑chain considerations.

Agent orchestration and lifecycle​

• Security Copilot provides both a no‑code agent builder (for analyst‑friendly creation) and a developer pathway (VS Code integrations, GitHub Copilot assistance) for pro‑code agents. Agents can be iteratively optimized from feedback loops and tuned via platform tooling. Microsoft’s Responsible AI and agent lifecycle guidance is available to administrators to set RBAC, agent identities, and approval gates.
• The MCP server, agent identity controls, and Azure AI Foundry lifecycle protections (task adherence, prompt shields, PII redaction) are intended to prevent runaway or misdirected agent actions while maintaining audit trails for compliance. These controls are still maturing and must be integrated into organizational change‑control processes.

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Strengths: what this architecture actually buys security teams​

• Richer context for AI and agents. Long‑tail telemetry plus graph relationships permits deeper correlation, higher‑precision detections, and better agent reasoning compared with short retention windows. This materially improves retrospective hunts and model training.
• Lowered engineering friction for integrations. MCP and a standardized Security Store aim to cut bespoke connector work and accelerate time‑to‑value for partner agents and no‑code automations.
• Faster SOC workflows and analyst uplift. Agents that triage, tune, and prioritize can reduce manual toil, improve mean time to triage, and free analysts for higher‑order threat hunting. Early Microsoft guidance highlights use cases such as phishing triage, vulnerability remediation, and conditional access optimization as high‑impact automation targets.
• Economics of retention. Separating a cost‑effective lake tier from hot analytics allows organizations to keep forensic data affordably, avoiding the need to choose between retention and real‑time indexing. This can be crucial for compliance and incident investigation.

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Risks, gaps, and practical caveats​

Centralizing telemetry concentrates risk​

A single, tenant‑level data lake and MCP endpoint becomes a high‑value target. If an adversary gains access to the lake or to agent identities with broad privileges, the impact escalates. Robust key management, least‑privilege RBAC, signed agent identities, and tenant‑side audit logging are not optional. Microsoft’s design includes tenant‑hosted MCP deployment options, but operators must still harden those control points.

Automated agents increase the blast radius of errors​

Agents that write or tune detection rules, triage alerts, or trigger remediation can speed up both good and bad actions. Misconfigured rules, over‑eager automated remediation, or faulty prioritization logic can cause business disruption (for example, widespread quarantines or poorly scoped policy changes). Build strong human‑in‑the‑loop approvals and rollback paths before enabling high‑impact agent actions.

Cost governance and runaway queries​

Graph traversals and large historical hunts can produce heavy query loads. Without query quotas, cost alerts, and scheduled heavy‑compute jobs, teams risk unexpected charges. The lake model introduces new meters (storage, ingestion, processing, query) that require forecasting and governance. Microsoft has published pricing previews, but organizations must validate queries and simulate workloads in test tenants.

Supply‑chain and marketplace trust​

A Security Store offers convenient distribution, but it requires vetting: partner agents will run inside sensitive environments and may request broad context. Validate third‑party agents, require code provenance, and maintain an approval pipeline. The Security Store model centralizes procurement but does not remove the need for rigorous vendor hygiene.

Claims that require careful validation​

Some vendor or partner statements (percent reductions in false positives, concrete MTTR improvements, or exact TCO numbers) are useful hypotheses but must be verified in each environment. Pilot measurements are essential—don’t accept blanket ROI claims without an evidence plan that includes realistic telemetry volumes and SOC workflows. Flag any numerical outcome quoted in marketing materials as contingent on workload, tuning, and safe rollout procedures.

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Practical rollout checklist for SOC teams (30–180 days)​

• Inventory and classify telemetry: map sources, retention needs, and regulatory constraints. Mark PII and regulated records and define redaction or pseudonymization rules before moving data to the lake.
• Start small: enable the Sentinel data lake for a controlled subset of tenants or workloads. Validate connectors and parsing to ensure data fidelity.
• Deploy MCP and agents in a tenant‑hosted test environment: instrument latency, availability SLAs, and provenance logging.
• Publish one low‑risk agent (e.g., triage summarizer) to a test workspace: measure false positives/negatives, analyst acceptability, and remediation accuracy.
• Hardening and adversarial testing: run prompt injection, malicious connector, and authorization mis‑use tests against the MCP and agent flows.
• Cost governance: configure query quotas, job scheduling, and cost alerts; estimate storage/processing/query meters for year‑one operations.
• Operationalize lifecycle: maintain versioned agent definitions, change approvals, retirement policies, and training for analysts on interpreting agent outputs.

These recommendations reflect the interplay between the data lake, graph and MCP primitives and the operational realities of SOC teams moving to agentic automation.

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The Avanade agents: realistic expectations and verification points​

Avanade’s Analytics Optimizer, Threat Shield, and Endpoint Risk Insights map to three common operational needs—noise reduction, intelligence‑driven coverage, and vulnerability prioritization. Organizations adopting these agents should verify, at a minimum:

• Baseline metrics for noisy alerts (pre‑agent) and measurable reduction targets (post‑agent).
• Coverage gaps that Threat Shield claims to close—compare threat intel feeds and detection rule coverage before and after deployment.
• Vulnerability prioritization accuracy from Endpoint Risk Insights—validate device lists against actual exploited CVEs and remediation success rates.

Independent press coverage and Avanade statements confirm the agents’ availability and design intent, but real efficacy will vary by environment and tuning. Plan instrumented pilots and measure outcomes against clearly defined KPIs.

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Governance, compliance, and responsible AI guardrails​

• Enforce agent identities backed by Entra (Azure AD) and least‑privilege RBAC with time‑bound approvals.
• Use Purview and DLP to classify and gate the data surface exposed to agents; enable PII detection and redaction where required.
• Maintain full provenance: every agent decision and action must be stamped with agent identity, model version, input context, and approval trace to serve auditors and incident investigations.
• Treat agents as software: maintain CI/CD for agent definitions, a model registry for approved models/MCP clients, and scheduled adversarial testing cycles.

Microsoft’s Responsible AI guidance for Security Copilot agents provides admin‑level controls and recommended practices, but those controls are platform constructs—organizational process and contractual guardrails remain critical.

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Final analysis: opportunity balanced with operational discipline​

Microsoft’s move to an agentic Sentinel—centered on a general‑availability data lake, a graph for relationship reasoning, and an MCP server for standardized agent access—represents a logical next step for modern SOCs. The platform reduces integration friction, opens the door to marketplace‑distributed agents, and gives AI models the historical context they need to find subtle, long‑dwell threats. Avanade’s design‑partner status and the debut of practical agents focused on noise reduction, threat intelligence coverage, and vulnerability prioritization are concrete early outcomes of that platform shift.
That opportunity comes with an equal measure of responsibility. Centralized telemetry and agentic automation amplify both the upside and the downside: faster detection and remediation are real, but so are systemic failure modes if governance, identity, and lifecycle controls are weak. The right path is measured adoption—define low‑risk pilots, instrument clear KPIs, harden the MCP and lake controls, and iterate using proven change control and adversarial testing practices. Organizations that treat the Sentinel data lake and agentic features as strategic operational programs, not point products, will capture the most value while managing the new risks.

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Quick reference: where to look first​

• If you are responsible for SOC tooling: stand up a test tenant, enable the Sentinel data lake for a subset of logs, and validate connector fidelity.
• If you are an engineering leader: instrument query quotas, cost alerts, and model versioning; require code provenance for any third‑party agents.
• If you are an information‑risk leader: map data flows from agents to regulatory obligations and enforce Purview labeling and DLP for agent payloads.

Microsoft’s product announcements and partner developments—including Avanade’s agent releases—provide powerful new building blocks for modern SOCs, but the business outcome depends on disciplined pilots, measurable KPIs, and rigorous governance.

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The Sentinel data lake, Sentinel graph, MCP runtime, Security Copilot agents, and partner agents (including Avanade’s initial agent set) mark a meaningful platform shift: they make agentic security operationally practical for the first time, provided organizations invest the same engineering rigor in governance, lifecycle, and adversarial testing that they apply to any other high‑value production system.

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Source: Cloud Wars AI Agent & Copilot Podcast: Avanade Exec on Security Data Storage, Integration Advances in Microsoft Sentinel