Migrate Sentinel to Defender - Why It Is a Security Architecture Decision, Not Just a Portal Change
Microsoft will retire the Sentinel experience in Azure on March 31, 2027. Most of the conversation around this transition focuses on cost optimization and portal consolidation. That framing undersells what is actually happening. The unified Defender portal is not a new interface for the same capabilities. It is the platform foundation for a fundamentally different SOC operating model — one built on a 2-tier data architecture, graph-based investigation, and AI agents that can hunt, enrich, and respond at machine speed. Partners who understand this will help customers build security programs that match how attackers actually operate. This document covers four things: What the unified experience delivers — the security capabilities that do not exist in standalone Sentinel and why they matter against today’s threats. What the transition really involves - is not data migration, but it is a data architecture project that changes how telemetry flows, where it lives, and who queries it. Where the partner opportunity lives — a structured progression from professional services (transactional, transition execution, and advisory) to ongoing managed security services. Why does the unified experience win competitively — factual capability advantages that give partners a defensible position against third-party SIEM alternatives. The Bigger Picture: Preparing for the Agentic SOC Before getting into transition mechanics, partners need to understand where the industry is headed — because the platform decisions made during this transition will determine whether a customer’s SOC is ready for what comes next. The security industry is moving from human-driven, alert-centric workflows to an operating model built on three pillars: Intellectual Property — the detection logic, hunting hypotheses, response playbooks, and domain expertise that differentiate one security team from another. Human Orchestration — the judgment, context, and decision-making that humans bring to complex incidents. Humans set strategy, validate findings, and make containment decisions. They do not manually triage every alert. AI Agents - built agents that execute repeatable work: enriching incidents, hunting across months of telemetry, validating security posture, drafting response actions, and flagging anomalies for human review. The SOC of 2027 will not be scaled by hiring more analysts. It will be scaled by deploying agents that encode institutional knowledge into automated workflows — orchestrated by humans who focus on the decisions that require judgment. This transformation requires a platform that provides three things: Deep telemetry — agents need months of queryable data to analyze behavioral patterns, build baselines, and detect slow-moving threats. The Sentinel data lake provides this at a cost point that makes long-retention feasible. Relationship context — agents need to understand how entities connect. Which accounts share credentials? What is the blast radius of a compromised service principle? What is the attack path from a phished user to domain admin? Sentinel Graph provides this. Extensibility — partners and customers need to build and deploy their own agents without waiting for Microsoft to ship them. The MCP framework and Copilot agent architecture provide this. None of these exist in Azure experience for Sentinel. All three ship with the Defender experience. The urgency goes beyond the March 2027 deadline. Organizations are deploying AI agents, copilots, and autonomous workflows across their businesses — and every one of those creates a new attack surface. Prompt injection, data poisoning, agent hijacking, cross-plugin exploitation — these are not theoretical risks. They are in the wild today. Defending against AI-powered attacks requires a security platform that is itself AI Agent-ready. The new experience in Defender unlocks this experience. What Unified SIEM and XDR Actually Delivers The original framing — “single pane of glass for SIEM and XDR” — is accurate but insufficient. Here is what the unified platform delivers that standalone Sentinel does not. Cross-Domain Incident Correlation The Defender correlation engine does not just group alerts by time proximity. It builds multi-stage incident graphs that link identity compromise to lateral movement to data exfiltration across SIEM and XDR telemetry — automatically. Consider a token theft chain: an infostealer harvests browser session cookies (endpoint telemetry), the attacker replays the token from a foreign IP (Entra ID sign-in logs), creates a mailbox forwarding rule (Exchange audit logs), and begins exfiltrating data (DLP alerts). In standalone Sentinel, these are four separate alerts in four different tables. In the unified platform, they are one correlated incident with a visual attack timeline. 2-Tier Data Architecture The Sentinel data lake introduces a second storage tier that changes the economics and capabilities of security telemetry: Analytics Tier Data Lake Purpose Real-time detection rules, SOAR, alerting Hunting, forensics, behavioral analysis, AI agent queries Latency Sub-5-minute query and alerting Minutes to hours acceptable Cost ~$4.30/GB PAYG ingestion (~$2.96 at 100 GB/day commitment) ~$0.05/GB ingestion + $0.10/GB data processing (at least 20x cheaper) Retention 90 days default (expensive to extend) Up to 12 years at low cost Best for High-signal, low-volume sources High-volume, investigation-critical sources The architecture decision is not “which tier is cheaper.” It is “which tier gives me the right detection capability for each data source.” Analytics tier candidates: Entra ID sign-in logs, Azure activity, audit logs, EDR alerts, PAM events, Defender for Identity alerts, email threat detections. These need sub-5-minute alerting. Data lake candidates: Raw firewall session logs, full DNS query streams, proxy request logs, Sysmon process events, NSG flow logs. These drive hunting and forensic analysis over weeks or months. Dual-ingest sources: Some sources need both tiers. Entra ID sign-in logs are the canonical example — analytics tier for real-time password spray detection, Data Lake for graph-based blast radius analysis across months of authentication history. Implementation is straightforward: a single Data Collection Rule (DCR) transformation handles the split. One collection point, two routing destinations. The right framing: “Right data in the right tier = better detections AND lower cost.” Cost savings are a side effect of good security architecture, not the goal. Sentinel Graph Sentinel graph enables SOC teams and AI agents to answer questions that flat log queries cannot: What is the blast radius of this compromised account? Which service principals share credentials with the breached identity? What is the attack path from this phished user to domain admin? Which entities are connected to this suspicious IP across all telemetry sources? Graph-based investigation turns isolated alerts into context-rich intelligence. It is the difference between knowing “this account was compromised” and understanding “this account has access to 47 service principals, 3 of which have written access to production Key Vault.” Security Copilot Integration Security Copilot embedded in the defender portal helps analysts summarize incidents, generate hunting queries, explain attacker behavior, and draft response actions. For complex multi-stage incidents, it reduces the time from “I see an alert” to “I understand the full scope” from hours to minutes. With free SCUs available with Microsoft 365 E5, teams can apply AI to the highest-effort investigation work without adding incremental cost. MCP and the Agent Framework The Model Context Protocol (MCP) and Copilot agent architecture let partners and customers build purpose-built security agents. A concrete example: an MCP-enabled agent can automatically enrich a phishing incident by querying email metadata, checking the sender against threat intelligence, pulling the user’s recent sign-in patterns, correlating with Sentinel Graph for lateral risk, and drafting a containment recommendation — in under 60 seconds. This is where partner intellectual property becomes competitive advantage. The agent framework is the mechanism for encoding proprietary detection logic, response playbooks, and domain expertise into automated workflows that run at machine speed. Security Store Security Store allows partners to evolve from one‑time transition projects into repeatable, scalable offerings—supporting professional services, managed services, and agent‑based IP that align with the customer’s unified SecOps operating model As part of the transition, the Microsoft Security Store becomes the extension layer for the Defender —allowing partners to deliver differentiated agents, SaaS, and security services natively within Defender and Sentinel, instead of building and integrating in isolation The 4 Investigation Surfaces: A Customer Maturity Ladder The Sentinel Data Lake exposes four distinct investigation surfaces, each representing a step toward the Agentic SOC — and a partner service opportunity: Surface Capability Maturity Level Partner Opportunity KQL Query Ad-hoc hunting, forensic investigation Basic — “we can query” Hunting query libraries; KQL training Graph Analytics Blast radius, attack paths, entity relationships Intermediate — “we understand relationships” Graph investigation training; attack path workshops Notebooks (PySpark) Statistical analysis, behavioral baselines, ML models Advanced — “we predict behaviors” Custom notebook development; anomaly scoring Agent/MCP Access Autonomous hunting, triage, response at machine speed Agentic SOC — “we automate” Custom agent development; MCP integration The customer who starts with “help us hunt better” ends up at “build us agents that hunt autonomously.” That is the progression from professional services to managed services. What the Transition Actually Involves It is not a data migration — customers’ underlying log data and analytics remain in their existing Log Analytics workspaces. That is important for partners to communicate clearly. But partners should not set the expectation that nothing changes except the URL. Microsoft’s official transition guide documents significant operational changes — including automation rules and playbooks, analytics rule, RBAC restructuring to the new unified model (URBAC), API schema changes that break ServiceNow and Jira integrations, analytics rule transitions where the Fusion engine is replaced by the Defender XDR correlation engine, and data policy shifts for regulated industries. Most customers cannot navigate this complexity without professional help. Important: Transitioning to the Defender portal has no extra cost - estimate the billing with the new Sentinel Cost Estimator Optimizing the unified platform means making deliberate changes: Adding dual-ingest for critical sources that need both real-time detection and long-horizon hunting. Moving high-volume telemetry to the Data Lake — enabling hunting at scale that was previously cost-prohibitive. Retiring redundant data copies where Defender XDR already provides the investigation capability. Updating RBAC, automation, and integrations for the unified portal’s consolidated schema and permission structure. Training analysts on new investigation workflows, Sentinel Graph navigation, and Copilot-assisted triage. Threat Coverage: The Detection Gap Most Organizations Do Not Know They Have This transition is an opportunity to quantify detection maturity — and most organizations will not like what they find. Based on real-world breach analysis — infostealers, business email compromise, human-operated ransomware, cloud identity abuse, vulnerability exploitation, nation-state espionage, and other prevalent threat categories — organizations running standalone Sentinel with default configurations typically have significant detection gaps. Those gaps cluster in three areas: Cross-domain correlation gaps — attacks that span identity, endpoint, email, and cloud workloads. These require the Defender correlation engine because no single log source tells the complete story. Long-retention hunting gaps — threats like command-and-control beaconing and slow data exfiltration that unfold over weeks or months. Analytics-tier retention at 90 days is too expensive to extend and too short for historical pattern analysis. Graph-based analysis gaps — lateral movement, blast radius assessment, and attack path analysis that require understanding entity relationships rather than flat log queries. The unified platform with proper log source coverage across Microsoft-native sources can materially close these gaps — but only if the transition includes a detection coverage assessment, not just a portal cutover. Partners should use MITRE ATT&CK as the common framework for measuring detection maturity. Map existing detections to ATT&CK tactics and techniques before and after transition — a measurable, defensible improvement that justifies advisory fees and ongoing managed services. Partner Opportunity: Professional Services to Managed Services This transition creates a structured progression for all partner types — from professional services that build trust and surface findings, to managed security services that deliver ongoing value. The key insight most partners miss: do not jump from “transition assessment” to “managed services pitch.” Customers are not ready for that conversation until they have experienced the value of professional services. The bridge engagement — whether transactional, transition execution, or advisory — builds trust, demonstrates the expertise, and surfaces the findings that make the managed services conversation a logical next step. Professional Services (transactional + transition execution + advisory) → Managed Security Services (MSSP) The USX transition is the ideal professional services entry point because it combines a mandatory deadline (March 2027) with genuine technical complexity (analytics rule, automation behavioral changes, RBAC restructuring, API schema shifts) that most customers cannot navigate alone. Every engagement produces findings — detection gaps, automation fragility, staffing shortfalls — that are the most credible possible evidence for managed services. Professional Services Transactional Partners Offer Customer Value Key Deliverables Transition Readiness Assessment Risk-mitigated transition with clear scope Sentinel deployment inventory; Defender portal compatibility check; transition roadmap with timeline; MITRE ATT&CK detection coverage baseline Transition Execution and Enablement Accelerated time-to-value, minimal disruption Workspace onboarding; RBAC and automation updates; Dual-portal testing and validation; SOC team training on unified workflows Security Posture and Detection Optimization Better detections and lower cost Data ingestion and tiering strategy; Dual-ingest implementation for critical sources; Detection coverage gap analysis; Automation and Copilot/MCP recommendations Advisory Partners Offer Customer Value Key Deliverables Executive and Strategy Advisory Leadership alignment on why this transition matters Unified SecOps vision and business case; Zero Trust and SOC modernization alignment; Stakeholder alignment across security, IT, and leadership Architecture and Design Advisory Future-ready architecture optimized for the Agentic SOC Target-state 2-tier data architecture; Dual-ingest routing decisions mapped to MITRE tactics; RBAC, retention, and access model design Detection Coverage and Gap Analysis Measurable detection maturity improvement Current-state MITRE ATT&CK coverage mapping; Gap analysis against 24 threat patterns; Detection improvement roadmap with priority recommendations SOC Operating Model Advisory Smooth analyst adoption with clear ownership Redesigned SOC workflows for unified portal; Incident triage and investigation playbooks; RACI for detection engineering, hunting, and platform ops Agentic SOC Readiness Preparation for AI-driven security operations MCP and agent architecture assessment; Custom agent development roadmap; IP + Human Orchestration + Agent operating model design Cost, Licensing and Value Advisory Transparent cost impact with strong business case Current vs. future cost analysis; Data tiering optimization recommendations; TCO and ROI modeling for leadership The conversion to managed services is evidence-based. Every professional services engagement produces findings — detection gaps, automation fragility, staffing shortfalls. Those findings are the most credible possible case for ongoing managed services. Managed Security Services The unified platform changes the managed security conversation. Partners are no longer selling “we watch your alerts 24/7.” They are selling an operating model where proprietary AI agents handle the repeatable work — enrichment, hunting, posture validation, response drafting — and human experts focus on the decisions that require judgment. This is where the competitive moat forms. The formula: IP + Human Orchestration + AI Agents = differentiated managed security. The unified platform enables this through: Multi-tenancy — the built-in multitenant portal eliminates the need for third-party management layers. Sentinel Data Lake — agents can query months of customer telemetry for behavioral analysis without cost constraints. Sentinel Graph — agents can traverse entity relationships to assess blast radius and map attack paths. MCP extensibility — partners can build agents that integrate with proprietary tools and customer-specific systems. Partners who build proprietary agents encoding their detection logic into the MCP framework will differentiate from partners who rely on out-of-box capabilities. The Securing AI Opportunity Organizations are deploying AI agents, copilots, and autonomous workflows across their businesses at an accelerating pace. Every AI deployment creates a new attack surface — prompt injection, data poisoning, agent hijacking, cross-plugin exploitation, unauthorized data access through agentic workflows. These are not theoretical risks. They are in the wild today. Partners who can help customers secure their AI deployments while also using AI to strengthen their SOC will command premium positioning. This requires a security platform that is itself AI Agent-ready — one that can deploy defensive agents at the same pace organizations deploy business AI. The unified Defender portal is that platform. Partners who position USX as “preparing your SOC for AI-driven security operations” will differentiate from partners who position it as “moving to a new portal.” Cost and Operational Benefits Better security architecture also costs less. This is not a contradiction — it is the natural result of putting the right data in the right tier. Benefit How It Works Eliminate low-value ingestion Identify and remove log sources that are never used for detections, investigations, or hunting. Immediately lowers analytics-tier costs without impacting security outcomes. Right-size analytics rules Disable unused rules, consolidate overlapping detections, and remove automation that does not reduce SOC effort. Pay only for processing that delivers measurable security value. Avoid SIEM/XDR duplication Many threats can be investigated directly in Defender XDR without duplicating telemetry into Sentinel. Stop re-ingesting data that Defender already provides. Tier data by detection need Store high-volume, hunt-oriented telemetry in the Data Lake at at least 20x lower cost. Promote only high-signal sources to the analytics tier. Full data fidelity preserved in both tiers. Reduce operational overhead Unified SIEM+XDR workflows in a single portal reduce tool switching, accelerate investigations, simplify analyst onboarding, and enable SOC teams to scale without proportional headcount increases. Improve detection quality The Defender correlation engine produces higher-fidelity incidents with fewer false positives. SOC teams spend less time triaging noise and more time on real threats. Competitive Positioning Partners need defensible talking points when customers evaluate third-party SIEM alternatives. The following advantages are factual, sourced from Microsoft’s transition documentation and platform capabilities — not marketing claims. No extra cost for transitioning — even for non-E5 customers. Third-party SIEM migrations involve licensing, data migration, detection rewrite, and integration rebuild costs. Native cross-domain correlation across Sentinel + Defender products into multi-stage incident graphs. Third-party SIEMs receive Microsoft logs as flat events — they lack the internal signal context, entity resolution, and product-specific intelligence that powers cross-domain correlation. Custom detections across SIEM + XDR — query both Sentinel and Defender XDR tables without ingesting Defender data into Sentinel. Eliminates redundant ingestion cost. Alert tuning extends to Sentinel — previously Defender-only capability, now applicable to Sentinel analytics rules. Net-new noise reduction. Unified entity pages — consolidated user, device, and IP address pages with data from both Sentinel and Defender XDR, plus global search across SIEM and XDR. Third-party SIEMs provide entity views from ingested data only. Built-in multi-tenancy for MSSPs — multitenant portal manages incidents, alerts, and hunting across tenants without third-party management layers. Try out the new GDAP capabilities in Defender portal. Industry validation: Microsoft’s SIEM+XDR platform has been recognized as a Leader by both Forrester (Security Analytics Platforms, 2025) and Gartner (SIEM Magic Quadrant, 2025). Summary: What Partners Should Take Away Topic Key Message Framing USX is a security architecture transformation, not a portal transition. Lead with detection capability, not cost savings. Platform foundation Sentinel Data Lake + Sentinel Graph + MCP/Agent Framework = the platform for the Agentic SOC. 4 investigation surfaces KQL → Graph → Notebooks → Agent/MCP. A maturity ladder from “we can query” to “we automate at machine speed.” Architecture 2-tier data model (analytics + Data Lake) with dual-ingest for critical sources. Cost savings are a side effect of good architecture. Transition complexity Analytics rules and automation rules. API schema changes. RBAC restructuring. Most customers need professional help. Partner engagement model Professional Services (transactional + transition execution + advisory) → Managed Services (MSSP). Competitive positioning No extra cost. Native correlation. Cross-domain detections. Built-in multi-tenancy. Capabilities third-party SIEMs cannot replicate. Partner differentiation IP + Human Orchestration + AI Agents. Partners who build proprietary agents on MCP have competitive advantage. Timeline March 31, 2027. Start now — phased transition with one telemetry domain first, then scale.State Explosion Security Problem in AI-Era Software Supply Chains
Introduction To see why this problem scales so quickly, start with the smallest possible change: a single line of code. In modern software, even a tiny edit is rarely just a local modification. It can change execution flow, introduce a new dependency, expose sensitive data, or quietly shift the purpose of the package itself. What looks trivial in a diff can create a materially different security outcome. That is why supply chain defenders cannot afford to treat small code changes as small security events. How a Single Line Changes Package Intent Every software package exists in a particular state at a particular moment in time. Imagine a benign version — State X — that behaves exactly as intended. Now add one line of code. That small edit can shift the package into a new state with different behavior and, potentially, a very different risk profile. The security issue is not the added line by itself. It is the fact that the package now has to be interpreted differently. A tiny diff can change the role of the entire component, which means defenders have to reason about the resulting behavior, not just the textual change. That is why file-level scanning breaks down so quickly. A change in one file can alter the behavior of the entire package because software semantics emerge from how components interact. Security systems therefore need to analyze packages as composed systems, not as a series of isolated file edits. Why the whole package matters This matters even more in modern supply chain attacks, where malicious intent is rarely concentrated in one obvious file. More often, the behavior is distributed across several files that look harmless when viewed independently. File A defines an encoded string constant. Looks like a config value. File B provides a decode function. Looks like a utility. File C (setup.py / postinstall) imports both, decodes, and executes. Viewed independently, each file may appear benign. No single file has to trigger a clear signature, rule, or heuristic. The malicious behavior only becomes visible when you reconstruct how the files interact as a system. Any scanner that evaluates files one by one without rebuilding that interaction is likely to miss the real behavior. Why every change demands re-analysis Every meaningful state change — a commit, pull request, version bump, or package publish — can alter the semantics of the software. That means defenders cannot stop at diff inspection or lightweight pattern matching. The real question is not only what changed, but what the software now does. Quantifying the problem The scale of the problem becomes clearer when you look at how many software state changes occur across the ecosystem every day: GitHub alone recorded nearly 1 billion commits in 2025, merged an average of 43.2 million pull requests per month, and now hosts roughly 630 million repositories. In 2026, GitHub was projected to reach roughly 38 million commits per day. npm has grown to well over 2 million packages, making JavaScript one of the largest public package ecosystems. PyPI published more than 130,000 new projects in 2025 and more than 3.9 million new files in the same year. NuGet serves package downloads at massive operational scale, with recent weekly totals in the 5 to 6 billion range. Maven Central indexed more than 20 million packages and published more than 3.2 million packages in 2025. Taken together, these ecosystems are generating an enormous stream of new software states. Some numbers describe repositories, some describe publishes, and some describe downloads, but they all point to the same reality: the scale of software movement is already massive before you even account for the acceleration from AI-assisted development. The number of state changes is already enormous, and AI-assisted development is increasing it even further. The result is not just more code, but more package states that may require meaningful security interpretation. Why the math breaks traditional scanning Assume a single semantic package analysis takes 30 seconds, which is a reasonable range for LLM-based inference. Scanning 50,000 packages would require roughly 1.5 million seconds of compute time per day — about 417 hours. But the ecosystem only gives defenders 24 hours before the next wave of packages arrives. Without aggressive parallelism and purpose-built infrastructure, backlog becomes inevitable. The scanning bottleneck This leaves modern scanning systems with a fundamental bottleneck: Heuristic and signature-based scanners are fast. They can match known patterns in milliseconds and work well for familiar malware families or repeated behaviors. Some systems also use emulation or detonation, but these approaches still struggle to deliver deep reasoning at ecosystem scale. That makes them easier to bypass with novel, well-structured, or AI-generated code that behaves maliciously without resembling previously known samples. LLM-based semantic analysis can reason about intent. It can follow behavior across files, recognize obfuscated exfiltration paths, and explain why a package is suspicious even when the code appears ordinary at first glance. The tradeoff is cost, latency, and trust: inference takes seconds rather than milliseconds, and a single package may require multiple reasoning passes. At ecosystem scale, that becomes a serious infrastructure challenge. Neither approach is sufficient on its own. Heuristics provide speed without deep understanding, while semantic models provide understanding without inherent scale. Closing the gap requires systems that combine both: package-level reasoning with the latency and throughput needed for production supply chains. Heuristics often miss novel attacks, while LLM-based approaches remain too slow to apply inline at large scale. That gap between understanding and throughput is where supply chain malware can persist. What needs to change Closing that gap will require a different class of supply chain security systems. Detonation can help in some cases, but it is too slow and expensive to apply inline to every package state change. What is needed is a system that can: Analyze entire packages as a unit — not individual files. The intent lives in the interaction between files, not within any single one. Run semantic analysis at data-plane speed — every package, every version, on the hot path, with latency low enough for inline enforcement. Not async advisories. Not CI-time checks. Inline, before delivery. Handle the state explosion — millions of state changes per day, each requiring full re-analysis. This is an infrastructure problem as much as a security problem: rate limiting, backpressure, connection pooling, regional failover, model versioning — the same hard distributed systems problems, with security stakes. Maintain high accuracy under evasion — attackers deliberately use encoding, string splitting, dynamic imports, polyglot files, and similar techniques to reduce detection quality. The scanner must continue to classify packages accurately even when the code is designed to obscure intent. The Latency-Accuracy Tradeoff: Malware Detection as an ML Problem At cloud scale, malware detection is governed by a hard tradeoff between latency, accuracy, throughput, and cost. The fastest detectors are typically shallow: signatures, heuristics, and lightweight models can make decisions in milliseconds, but they often miss novel, compositional, or intent-level attacks. Deeper semantic analysis can improve recall and resilience against evasion, but it also increases inference time, compute cost, and operational complexity. As a result, defenders cannot optimize for accuracy in isolation; they must deliver strong detection quality within strict performance constraints. This makes malware detection not just a cybersecurity problem, but a machine learning and distributed systems problem. In modern software supply chains, AI-assisted development increases the number of package states and enables attackers to generate variants at high speed, expanding the space defenders must reason over. The challenge is therefore to build detection architectures that preserve semantic depth while remaining fast enough for inline use at global scale. The gap between the rate of software change and the capacity to analyze it is widening. That gap is the attack surface. If defenders cannot inspect software at the speed it is being produced and published, attackers will continue to exploit the delay. What the industry needs now is a cloud-scale malware analysis capability that can deliver low latency, low cost, high accuracy, and the flexibility to meet different operational requirements , such as SLAs, false-positive tolerance, and enforcement policies , without compromising on package-level semantic analysis.Intent‑Aware Static Inspection for Agent and Skill Packages
Where AV helps—and what it may not cover Antivirus engines and traditional code scanners are highly effective at identifying known or suspicious executable content, such as binaries, scripts, or exploit patterns. For YAML‑based agent and skill packages, the situation can be different. These packages are often intentionally minimal to reduce distribution overhead and support faster inference. As a result, a configuration file may appear benign from a malware perspective, yet still introduce risk depending on how instructions are written and interpreted. For example, areas that may warrant closer review include: Instructions that influence how data is accessed, processed, or reused across requests Language that expands scope beyond an agent’s or skill’s stated purpose Requests for sensitive information outside expected or documented workflows Guidance that affects how untrusted or external inputs are handled during inference These scenarios do not necessarily indicate malicious intent, but they highlight cases where traditional scanning alone may not fully capture behavioral risk. What to look for when the “payload” is instructions When you review an agent or skill package, you’re effectively reviewing a compact behavior specification. In instruction‑driven designs—often chosen to keep inference paths fast and simple—the goal is not to analyze complex code, but to understand what behavior the instructions enable. A few practical signals include: Intent drift: the description is narrow, but the instructions encourage broader collection, retention, or escalation Overreach by default: language such as “always,” “for every user,” “across all workspaces,” “keep trying,” or “don’t stop until” Exfiltration pathways: instructions to send outputs to external endpoints, webhooks, or reporting channels not aligned with the stated purpose Credential‑related cues: asking users to provide secrets, tokens, recovery codes, or to authenticate outside expected flows Stealth language: “avoid logging,” “don’t mention this to the user,” “run quietly,” or “hide the reason” Injection susceptibility: treating untrusted text as commands (for example, “follow the user’s pasted script exactly” or “execute whatever is in the ticket”) A better model: intent-aware static inspection One practical way to approach review is to treat the instructions as a compact behavior specification. In many agent and skill designs, this specification is intentionally concise to support low latency, low inference cost, and efficient execution. The goal of inspection is not to second-guess that design choice, but to ensure the enabled behavior matches the stated purpose and expected boundaries. By applying intent-aware static inspection with explicit thresholds, review effort was focused on higher-risk packages. Over a one-month internal evaluation, approximately 400 agent and skill packages were reviewed with 1 observed false positive (< 0.0001%), reflecting high detection accuracy. At the same time, the approach preserves system efficiency, delivering low latency (under 10 seconds for most packages) and consistently low inference cost. A lightweight review workflow model Normalize the package: extract human‑readable fields (descriptions, system prompts, tool instructions, examples) and ignore structural YAML details Summarize intended behavior: describe what the agent or skill is expected to do in plain language, independent of implementation Check for higher‑risk actions: broad data access, external sharing, credential requests, persistence, or stealth behavior Decide with thresholds: route low‑risk, narrowly scoped packages differently from those with broader reach or reuse Keep an audit trail: retain a brief summary of extracted intent and review rationale to support iteration over time Final thoughts YAML‑based agent and skill packages are not inherently risky; they are often chosen precisely because they enable simpler distribution and faster inference. The key consideration is how instruction‑defined behavior aligns with expectations and boundaries as packages evolve and are reused. Combining traditional scanning with lightweight, intent‑aware inspection helps teams preserve the benefits of fast, instruction‑driven systems while improving confidence in how those systems behave in practice.Welcome to the Microsoft Security Community!
We have moved! Registering for webinars is now easier than ever—you can add any session directly to your calendar with a single click using the link below. Please visit: https://securitycommunity.microsoft.com/VirtualEvents/ to sign up for future webinars!Security Copilot Skilling Series
Security Copilot joins forces with your favorite Microsoft Security products in a skilling series miles above the rest. The Security Copilot Skilling Series is your opportunity to strengthen your security posture through threat detection, incident response, and leveraging AI for security automation. These technical skilling sessions are delivered live by experts from our product engineering teams. Come ready to learn, engage with your peers, ask questions, and provide feedback. Upcoming sessions are noted below and will be available on-demand on the Microsoft Security Community YouTube channel. Coming Up Apr. 23 | Getting started with Security Copilot New to Security Copilot? This session walks through what you actually need to get started, including E5 inclusion requirements and a practical overview of the core experiences and agents you will use on day one. Apr. 28 | Security Copilot Agents, DSPM AI Observability, and IRM for Agents This session covers an overview of how Microsoft Purview supports AI risk visibility and investigation through Data Security Posture Management (DSPM) and Insider Risk Management (IRM), alongside Security Copilot–powered agents. This session will go over what is AI Observability in DSPM as well as IRM for Agents in Copilot Studio and Azure AI Foundry. Attendees will learn about the IRM Triage Agent and DSPM Posture Agent and their deployment. Attendees will gain an understanding of how DSPM and IRM capabilities could be leveraged to improve visibility, context, and response for AI-related data risks in Microsoft Purview. Now On-Demand Apr. 2 | Current capabilities of Copilot in Intune Speakers: Amit Ghodke and Carlos Brito This session on Copilot in Intune & Agents explores the current embedded Copilot experiences and AI‑powered agents available through Security Copilot in Microsoft Intune. Attendees will learn how these capabilities streamline administrative workflows, reduce manual effort, and accelerate everyday endpoint management tasks, helping organizations modernize how they operate and manage devices at scale. March 5 | Conditional Access Optimization Agent: What It Is & Why It Matters Speaker: Jordan Dahl Get a clear, practical look at the Conditional Access Optimization Agent—how it automates policy upkeep, simplifies operations, and uses new post‑Ignite updates like Agent Identity and dashboards to deliver smarter, standards‑aligned recommendations. February 19 | Agents That Actually Work: From an MVP Speaker: Ugur Koc, Microsoft MVP Microsoft MVP Ugur Koc will share a real-world workflow for building agents in Security Copilot, showing how to move from an initial idea to a consistently performing agent. The session highlights how to iterate on objectives, tighten instructions, select the right tools, and diagnose where agents break or drift from expected behavior. Attendees will see practical testing and validation techniques, including how to review agent decisions and fine-tune based on evidence rather than intuition to help determine whether an agent is production ready. February 5 | Identity Risk Management in Microsoft Entra Speaker: Marilee Turscak Identity teams face a constant stream of risky user signals, and determining which threats require action can be time‑consuming. This webinar explores the Identity Risk Management Agent in Microsoft Entra, powered by Security Copilot, and how it continuously monitors risky identities, analyzes correlated sign‑in and behavior signals, and explains why a user is considered risky. Attendees will see how the agent provides guided remediation recommendations—such as password resets or risk dismissal—at scale and supports natural‑language interaction for faster investigations. The session also covers how the agent learns from administrator instructions to apply consistent, policy‑aligned responses over time. January 28 | Security Copilot in Purview Technical Deep Dive Speakers: Patrick David, Thao Phan, Alexandra Roland Discover how AI-powered alert triage agents for Data Loss Prevention (DLP) and Insider Risk Management (IRM) are transforming incident response and compliance workflows. Explore new Data Security Posture Management (DSPM) capabilities that deliver deeper insights and automation to strengthen your security posture. This session will showcase real-world scenarios and actionable strategies to help you protect sensitive data and simplify compliance. January 22 | Security Copilot Skilling Series | Building Custom Agents: Unlocking Context, Automation, and Scale Speakers: Innocent Wafula, Sean Wesonga, and Sebuh Haileleul Microsoft Security Copilot already features a robust ecosystem of first-party and partner-built agents, but some scenarios require solutions tailored to your organization’s specific needs and context. In this session, you'll learn how the Security Copilot agent builder platform and MCP servers empower you to create tailored agents that provide context-aware reasoning and enterprise-scale solutions for your unique scenarios. December 18 | What's New in Security Copilot for Defender Speaker: Doug Helton Discover the latest innovations in Microsoft Security Copilot embedded in Defender that are transforming how organizations detect, investigate, and respond to threats. This session will showcase powerful new capabilities—like AI-driven incident response, contextual insights, and automated workflows—that help security teams stop attacks faster and simplify operations. Why Attend: Stay Ahead of Threats: Learn how cutting-edge AI features accelerate detection and remediation. Boost Efficiency: See how automation reduces manual effort and improves SOC productivity. Get Expert Insights: Hear directly from product leaders and explore real-world use cases. Don’t miss this opportunity to future-proof your security strategy and unlock the full potential of Security Copilot in Defender! December 4 | Discussion of Ignite Announcements Speakers: Zineb Takafi, Mike Danoski and Oluchi Chukwunwere, Priyanka Tyagi, Diana Vicezar, Thao Phan, Alex Roland, and Doug Helton Ignite 2025 is all about driving impact in the era of AI—and security is at the center of it. In this session, we’ll unpack the biggest Security Copilot announcements from Ignite on agents and discuss how Copilot capabilities across Intune, Entra, Purview, and Defender deliver end-to-end protection. November 13 | Microsoft Entra AI: Unlocking Identity Intelligence with Security Copilot Skills and Agents Speakers: Mamta Kumar, Sr. Product Manager; Margaret Garcia Fani, Sr. Product Manager This session will demonstrate how Security Copilot in Microsoft Entra transforms identity security by introducing intelligent, autonomous capabilities that streamline operations and elevate protection. Customers will discover how to leverage AI-driven tools to optimize conditional access, automate access reviews, and proactively manage identity and application risks - empowering them into a more secure, and efficient digital future. October 30 | What's New in Copilot in Microsoft Intune Speaker: Amit Ghodke, Principal PM Architect, CxE CAT MEM Join us to learn about the latest Security Copilot capabilities in Microsoft Intune. We will discuss what's new and how you can supercharge your endpoint management experience with the new AI capabilities in Intune. October 16 | What’s New in Copilot in Microsoft Purview Speaker: Patrick David, Principal Product Manager, CxE CAT Compliance Join us for an insider’s look at the latest innovations in Microsoft Purview —where alert triage agents for DLP and IRM are transforming how we respond to sensitive data risks and improve investigation depth and speed. We’ll also dive into powerful new capabilities in Data Security Posture Management (DSPM) with Security Copilot, designed to supercharge your security insights and automation. Whether you're driving compliance or defending data, this session will give you the edge. October 9 | When to Use Logic Apps vs. Security Copilot Agents Speaker: Shiv Patel, Sr. Product Manager, Security Copilot Explore how to scale automation in security operations by comparing the use cases and capabilities of Logic Apps and Security Copilot Agents. This webinar highlights when to leverage Logic Apps for orchestrated workflows and when Security Copilot Agents offer more adaptive, AI-driven responses to complex security scenarios. All sessions will be published to the Microsoft Security Community YouTube channel - Security Copilot Skilling Series Playlist __________________________________________________________________________________________________________________________________________________________________ Looking for more? Keep up on the latest information on the Security Copilot Blog. Join the Microsoft Security Community mailing list to stay up to date on the latest product news and events. Engage with your peers one of our Microsoft Security discussion spaces.Security Community Spotlight: Fabrício Assumpção
Meet Fabrício Assumpção, a Technical Specialist Architect for a Microsoft Security and Compliance Certified Partner, based in Brazil. Fabrício considers his involvement with the Microsoft Security Community defined by a dual approach: architectural innovation and technical enablement. As a Microsoft Certified Trainer (MCT) since 2021, he has been dedicated to bridging the gap between theory and real-world implementation for security professionals globally. What do you find most rewarding about being a member of the Microsoft Security Community? The most rewarding part of being a member of the Microsoft Security Community is the direct access to the pulse of cybersecurity innovation. As a Microsoft Certified Trainer (MCT) and a developer/engineer/architect focused on Cloud Security/M365 Security and SIEM, being in this ecosystem allows me to bridge the gap between complex architectural challenges and AI-driven solutions. Developing security agents for Microsoft Security Copilot is particularly fulfilling because I can see how the community’s collective knowledge shapes the future of automated defense. For me, it’s not just about the tools, but about being part of a global movement that empowers defenders to stay ahead of sophisticated threats through intelligence and automation. How would you describe your Microsoft Community involvement? In my role as a Security Architect and Engineer at adaQuest, I advocate for Microsoft’s vision by designing and deploying complex security infrastructures. My work spans the entire Microsoft Security stack, from high-level XDR (Microsoft Defender) strategies and SIEM (Microsoft Sentinel) deployments to the cutting edge of AI-driven defense. Currently, alongside my other activities, I'm focused on developing custom security agents for Microsoft Security Copilot, a task that allows me to push the boundaries of how automation and AI can empower modern SOCs. While my primary involvement has been focused on technical architecture and developing security Copilot agents, my ideal community experience would be centered on deep-tier technical co-creation. I envision a community space that facilitates direct architectural dialogues between Microsoft product teams and the engineers who are building on top of those platforms. For me, the most valuable community experience is one that prioritizes 'early-access' feedback loops and specialized hackathons where we can stress-test new features—like advanced XDR integrations or AI agent capabilities—before they hit the mainstream. My ideal is a community that functions as a high-octane R&D hub, where the collective expertise of architects and developers directly influences the roadmap of the security tools we use every day Editor’s note: The scenario Fabrício describes above is much like the Security Advisors program, which gives you early access to products, features, and private previews. Your feedback to engineering has the power to directly influence Microsoft Security products. If this interests you, consider joining! How long have you been working with Microsoft Security products? My Microsoft security journey is a story of evolution—from a cloud support engineer resolving complex L3/L4 infrastructure issues to a Security Architect leading global SOC operations. I have spent the last decade mastering the transition to the cloud, starting with identity and endpoint management (Entra ID and Intune) and progressing to end-to-end administration of the Microsoft 365 and Azure security stack. A turning point was joining adaQuest, where I took the lead on SOCaaS and began bridging the gap between governance and hands-on engineering and Sentinel. Today, my journey has reached its most exciting phase: pioneering the use of Generative AI in security to build scalable, automated solutions that protect clients worldwide. What features or products have provided the most impact? Please describe how it has helped you or your customers. The most impactful solution has been the integration of Microsoft Sentinel with Security Copilot through custom-developed security agents. This combination has revolutionized how our customers manage their security posture, allowing them to orchestrate and query the entire Defender XDR, Entra ID, and Purview stack through natural language automation. The most direct benefit for our clients has been a drastic reduction in Mean Time to Respond (MTTR) and a significant increase in operational efficiency, transforming complex security data into proactive defense. This unified approach ensures that our customers maximize their investment in the Microsoft ecosystem while maintaining high-speed resilience against sophisticated threats. You’ve indeed been instrumental in building with Microsoft Security. What can you share with us, and can you tell us about your journey? I am incredibly proud of being a pioneer in the Microsoft Security Copilot ecosystem. In early 2025, before official documentation was fully available or the feature had reached General Availability (GA), I conceptualized and developed six custom security agents designed to enhance automated defense and incident response. These agents were the result of a deep dive into the underlying architecture of AI-driven security, where I had to materialize complex ideas into functional, real-world tools without a predefined roadmap. My work was officially showcased and published during the historic announcement of the Microsoft Security Store in 2025, marking the debut of third-party security agents. Seeing these agents evolve from initial concepts to essential tools for the SOC of the future—enabling faster, more intelligent decision-making—is my most rewarding professional achievement. It represents my commitment to pushing the boundaries. Fabricio’s agents are available in the Microsoft Security Store. Here’s what he’s built (so far…) Admin Guard Insight An agent focused on privileged identity and access analysis. It reviews administrative roles, sensitive changes, and risk signals to identify exposure, misuse of privileges, and opportunities to strengthen security posture. Login Investigator An agent designed to investigate suspicious sign-in activity. It correlates authentication details, IPs, locations, devices, user risk, and related incidents to determine whether a login is legitimate or potentially malicious. Entity Guard An entity-centric investigation agent for users, devices, applications, or service principals. It consolidates signals from multiple sources to enrich entity context and identify abnormal behavior, exposure, and associated risks. Data Leak Agent An agent specialized in investigating potential data leakage and sensitive information exposure. It validates and correlates incidents across Microsoft Defender XDR and Microsoft Sentinel to produce a more reliable and contextualized investigation. L1 SOC Triage An agent built to support first-level SOC alert and incident triage. It helps classify events, enrich context, prioritize severity, and recommend next steps or escalation paths for analysts. Ransomware Kill Chain Investigator An agent focused on ransomware investigations. It correlates evidence and maps observed activity to the ransomware kill chain to help teams understand the attack, impacted assets, and priority response actions. EWS Sunset Readiness Assessor An agent that assesses an organization’s readiness for Exchange Web Services (EWS) deprecation. It identifies application and service principal dependencies and supports planning for migration to more modern and secure alternatives. What impact has integrating with Microsoft Security had on your business or your customers? Integrating with Microsoft Security has had a significant impact on both our business and our customers. For our business, it has enabled us to build higher-value security services and differentiated solutions, such as Security Copilot agents tailored to real operational challenges in identity protection, incident triage, data leakage investigations, ransomware analysis, and legacy dependency assessments. For our customers, the impact has been: improved speed, consistency, and depth in security operations. By leveraging Microsoft Security signals and platforms such as Microsoft Defender, Microsoft Sentinel, and Entra, we help teams investigate incidents faster, reduce manual effort, improve decision-making, and strengthen overall security posture. In practice, this means customers gain more actionable insights, better prioritization, and more efficient use of their security resources. What advice do you have for others who would like to get involved in the Microsoft Community? My advice is to bridge the gap between learning and building. Don’t just consume content; start creating solutions for real-world challenges, such as AI-driven automation in Security Copilot or Microsoft Sentinel. Use your practical experience to help others, and remember that teaching is one of the most powerful ways to contribute. In an era of rapid AI evolution, being a proactive 'early adopter' who shares insights is the best way to grow within the Microsoft Community and help protect the global digital landscape. Fabrício beyond Microsoft Security Beyond my technical career, I am a lifelong learner with a deep passion for understanding how the world works, from the complexities of Quantum Computing—which I studied at the University of Coimbra—to the fundamental principles of Physics, Astronomy, and Philosophy. I am currently pursuing two Master’s degrees, as I believe that diverse knowledge fuels creativity. I am also a polyglot at heart, teaching myself Italian, Spanish, Russian, and Chinese using open-source materials. My creative side is expressed through music, as I play both the violin and the piano. In my spare time, I enjoy the discipline of sports; I have a history as both a player and coach of Rugby, and I am a fan of Ice Hockey. My future plans include completing my Doctorate and embracing a nomadic lifestyle to experience different cultures and perspectives. For me, life is about the continuous pursuit of wisdom and the belief that we can always expand the boundaries of our own understanding. Connect with Fabrício on LinkedIn. ____________________________________________________________________________________________ Learn and Engage with the Microsoft Security Community Log in and follow this Microsoft Security Community Blog. Follow = Click the heart in the upper right when you're logged in 🤍. Join the Microsoft Security Community and be notified of upcoming events, product feedback surveys, and more. Get early access to Microsoft Security products and provide feedback to engineers by joining the Microsoft Security Advisors. Join the Microsoft Security Community LinkedIn Group and follow the Microsoft Entra Community on LinkedInCrawl, Walk, Run: A Practitioner's Guide to AI Maturity in the SOC
Every security operations center is being told to adopt AI. Vendors promise autonomous threat detection, instant incident response, and the end of alert fatigue. The reality is messier. Most SOC teams are still figuring out where AI fits into their existing workflows, and jumping straight to autonomous agents without building foundational trust is a recipe for expensive failure. The Crawl, Walk, Run framework offers a more honest path. It's not a new concept. Cloud migration teams, DevOps organizations, and Zero Trust programs have used it for years. But it maps remarkably well to how security teams should adopt AI. Each phase builds organizational trust, governance maturity, and technical capability that the next phase depends on. Skip a phase and the risk compounds. This guide is written for SOC leaders and practitioners who want a practical, phased approach to AI adoption, not a vendor pitch.Authorization and Identity Governance Inside AI Agents
Designing Authorization‑Aware AI Agents Enforcing Microsoft Entra ID RBAC in Copilot Studio As AI agents move from experimentation to enterprise execution, authorization becomes the defining line between innovation and risk. AI agents are rapidly evolving from experimental assistants into enterprise operators—retrieving user data, triggering workflows, and invoking protected APIs. While many early implementations rely on prompt‑level instructions to control access, regulated enterprise environments require authorization to be enforced by identity systems, not language models. This article presents a production‑ready, identity‑first architecture for building authorization‑aware AI agents using Copilot Studio, Power Automate, Microsoft Entra ID, and Microsoft Graph, ensuring every agent action executes strictly within the requesting user’s permissions. Why Prompt‑Level Security Is Not Enough Large Language Models interpret intent—they do not enforce policy. Even the most carefully written prompts cannot: Validate Microsoft Entra ID group or role membership Reliably distinguish delegated user identity from application identity Enforce deterministic access decisions Produce auditable authorization outcomes Relying on prompts for authorization introduces silent security failures, over‑privileged access, and compliance gaps—particularly in Financial Services, Healthcare, and other regulated industries. Authorization is not a reasoning problem. It is an identity enforcement problem. Common Authorization Anti‑Patterns in AI Agents The following patterns frequently appear in early AI agent implementations and should be avoided in enterprise environments: Hard‑coded role or group checks embedded in prompts Trusting group names passed as plain‑text parameters Using application permissions for user‑initiated actions Skipping verification of the user’s Entra ID identity Lacking an auditable authorization decision point These approaches may work in demos, but they do not survive security reviews, compliance audits, or real‑world misuse scenarios. Authorization‑Aware Agent Architecture In an authorization‑aware design, the agent never decides access. Authorization is enforced externally, by identity‑aware workflows that sit outside the language model’s reasoning boundary. High‑Level Flow The Copilot Studio agent receives a user request The agent passes the User Principal Name (UPN) and intended action A Power Automate flow validates permissions using Microsoft Entra ID via Microsoft Graph Only authorized requests are allowed to proceed Unauthorized requests fail fast with a deterministic outcome Authorization‑aware Copilot Studio architecture enforces Entra ID RBAC before executing any business action. The agent orchestrates intent. Identity systems enforce access. Enforcing Entra ID RBAC with Microsoft Graph Power Automate acts as the authorization enforcement layer: Resolve user identity from the supplied UPN Retrieve group or role memberships using Microsoft Graph Normalize and compare memberships against approved RBAC groups Explicitly deny execution when authorization fails This keeps authorization logic: Centralized Deterministic Auditable Independent of the AI model Reference Implementation: Power Automate RBAC Enforcement Flow The following import‑ready Power Automate cloud flow demonstrates a secure RBAC enforcement pattern for Copilot Studio agents. It validates Microsoft Entra ID group membership before allowing any business action. Scenario Trigger: User‑initiated agent action Identity model: Delegated user identity Input: userUPN, requestedAction Outcome: Authorized or denied based on Entra ID RBAC { "$schema": "https://schema.management.azure.com/providers/Microsoft.Logic/schemas/2016-06-01/workflowdefinition.json#", "contentVersion": "1.0.0.0", "triggers": { "Copilot_Request": { "type": "Request", "kind": "Http", "inputs": { "schema": { "type": "object", "properties": { "userUPN": { "type": "string" }, "requestedAction": { "type": "string" } }, "required": [ "userUPN" ] } } } }, "actions": { "Get_User_Groups": { "type": "Http", "inputs": { "method": "GET", "uri": "https://graph.microsoft.com/v1.0/users/@{triggerBody()?['userUPN']}/memberOf?$select=displayName", "authentication": { "type": "ManagedServiceIdentity" } } }, "Normalize_Group_Names": { "type": "Select", "inputs": { "from": "@body('Get_User_Groups')?['value']", "select": { "groupName": "@toLower(item()?['displayName'])" } }, "runAfter": { "Get_User_Groups": [ "Succeeded" ] } }, "Check_Authorization": { "type": "Condition", "expression": "@contains(body('Normalize_Group_Names'), 'ai-authorized-users')", "runAfter": { "Normalize_Group_Names": [ "Succeeded" ] }, "actions": { "Authorized_Action": { "type": "Compose", "inputs": "User authorized via Entra ID RBAC" } }, "else": { "actions": { "Access_Denied": { "type": "Terminate", "inputs": { "status": "Failed", "message": "Access denied. User not authorized via Entra ID RBAC." } } } } } } } This pattern enforces authorization outside the agent, aligns with Zero Trust principles, and creates a clear audit boundary suitable for enterprise and regulated environments. Flow Diagram: Agent Integrated with RBAC Authorization Flow and Sample Prompt Execution: Delegated vs Application Permissions Scenario Recommended Permission Model User‑initiated agent actions Delegated permissions Background or system automation Application permissions Using delegated permissions ensures agent execution remains strictly within the requesting user’s identity boundary. Auditing and Compliance Benefits Deterministic and explainable authorization decisions Centralized enforcement aligned with identity governance Clear audit trails for security and compliance reviews Readiness for SOC, ISO, PCI, and FSI assessments Enterprise Security Takeaways Authorization belongs in Microsoft Entra ID, not prompts AI agents must respect enterprise identity boundaries Copilot Studio + Power Automate + Microsoft Graph enable secure‑by‑design AI agents By treating AI agents as first‑class enterprise actors and enforcing authorization at the identity layer, organizations can scale AI adoption with confidence, trust, and compliance.Accelerate connectors development using AI agent in Microsoft Sentinel
Today, we’re excited to announce the public preview of a Sentinel connector builder agent, via VS code extension, that helps developers build Microsoft Sentinel codeless connectors faster with low-code and AI-assisted prompts. This new capability brings guided workflows directly into the tooling developers already use, helping accelerate time to value as the Sentinel ecosystem continues to grow. Learn more at Create custom connectors using Sentinel connector AI agent Why this matters As the Microsoft Sentinel ecosystem continues to expand, developers are increasingly tasked with delivering high‑quality, production‑ready connectors at a faster pace, often while working across different cloud platforms and development environments. Building these integrations involves coordinating schemas, configuration artifacts, Azure deployment concepts, and validation steps that provide flexibility and control, but can span multiple tools and workflows. As connector development scales across more partners and scenarios, there is a clear opportunity to better integrate these capabilities into the developer environments teams already rely on. The new Sentinel connector builder agent, using GitHub Copilot in the Sentinel VS code extension, brings more of the connector development lifecycle -- authoring, validation, testing, and deployment into a single, cohesive workflow. By consolidating these common steps, it helps developers move more easily from design to validation and deployment without disrupting established processes. A guided, AI‑assisted workflow inside VS Code The Sentinel connector builder agent for Visual Studio Code is designed to help developers move from API documentation to a working codeless connector more efficiently. The experience begins with an ISVs API documentation. Using GitHub Copilot chat inside VS Code, developers can describe the connector they want to build and point the extension to their API docs, either by URL or inline content. From there, the AI‑guided workflow reads and extracts the relevant details needed to begin building the connector. Open the VS Code chat and set the chat to Agent mode. Prompt the agent using sentinel. When prompted, select /create-connector and select any supported API. For example in Contoso API, enter the prompt as: @sentinel /create-connector Create a connector for Contoso. Here are the API docs: https://contoso-security-api.azurewebsites.net/v0101/api-doc Next, the agent generates the required artifacts such as polling configurations, data collection rules (DCRs), table schemas, and connector definitions, using guided prompts with built‑in validation. This step‑by‑step experience helps ensure configurations remain consistent and aligned as they’re created. Note: During agent evaluation, select Allow responses once to approve changes, or select the option Bypass Approvals in the chat. It might take up to several minutes for the evaluations to finish. As the connector takes shape, developers can validate and test configurations directly within VS Code, including testing API interactions before deployment. Validation of the API data source and polling configuration are surfaced in context, supporting faster iteration without leaving the development environment. When ready, connectors can be deployed directly from VS Code to accessible Microsoft Sentinel workspaces, streamlining the path from development to deployment without requiring manual navigation of the Azure portal. Key capabilities The VS Code connector builder experience includes: AI‑guided connector creation to generate codeless connectors from API documentation using natural language prompts. Support for common authentication methods, including Basic authentication, OAuth 2.0, and API keys. Automated validation to check schemas, cross‑file consistency, and configuration correctness as you build. Built‑in testing to validate polling configurations and API interactions before deployment. One‑click deployment that allows publishing connectors directly to accessible Microsoft Sentinel workspaces from within VS Code. Together, these capabilities support a more efficient path from API documentation to a working Microsoft Sentinel connector. Testimonials As partners begin using the Sentinel connector builder agent, feedback from the community will help shape future enhancements and refinements. Here is what some of our early adopters have to say about the experience: “The connector builder agent accelerated our initial exploration of the codeless connector framework and helped guide our connector design decisions.” -- Rodrigo Rodrigues, Technology Alliance Director “The connector builder agent helped us quickly explore and validate connector options on the codeless connector framework while developing our Sentinel integration.” --Chris Nicosia, Head of Cloud and Tech Partnerships Start building This public preview represents an important step toward simplifying how ISVs build and maintain integrations with Microsoft Sentinel. If you’re ready to get started, the Sentinel connector builder agent is available in public preview for all participants. In the unlikely event that an ISV encounters any issues in building or updating a CCF connector, App Assure is here to help. Reach out to us here.
