The CISO Cheat Sheet to Microsoft Digital Defense Report 2026
I know we’re all busy, so I’ve pulled the most important data from this year’s Microsoft Digital Defense Report into one short read. The full report is well worth exploring, but this article focuses on the numbers and trends most relevant to security leaders. This year’s edition shows that AI is lowering the time, expertise and cost required to discover and exploit weaknesses, enabling attackers to operate with greater speed, scale and autonomy. The report describes this as AI “changing the physics of cybersecurity”: attack timelines are compressing and agentic systems are beginning to automate more of the attack lifecycle. Trend 1: Time has collapsed The clearest finding is about speed. Vulnerability discovery is increasingly running ahead of remediation, while the time from discovery to weaponisation is shrinking. In the AI era, weaponisation can follow in hours. Vulnerability discovery and weaponisation now often needs only a well-written prompt. The median time from discovery in the wild to weaponisation is below 24 hours. Vulnerabilities were on track for a record estimated 72,000 CVEs in 2026. The report expects a multi-year period in which the number of known but unpatched vulnerabilities rises sharply before eventually declining. The median time from container start to first exploit attempt is 5.3 hours. The ransomware actor Storm-1175 moved from web-facing exploitation to deploying Medusa ransomware in as little as 24 hours. CISA added over 110 CVEs to its Known Exploited Vulnerabilities catalogue between November 2025 and May 2026, most within a week of disclosure. The mismatch with enterprise reality is the uncomfortable part. The report says the disclosure-to-exploitation window has shrunk to single-digit days. Between November 2025 and May 2026, CISA added more than 110 vulnerabilities to its Known Exploited Vulnerabilities catalogue, most within a week of disclosure. In one example, Storm-1175 moved from exploiting a web-facing vulnerability to deploying Medusa ransomware in as little as 24 hours. Microsoft’s Red Team makes an important point: a patch is also a disclosure. Every fix points attackers towards the flaw it closes. As AI-assisted analysis makes reversing patches faster and more reliable, the report argues that patch velocity, not simply patch availability, becomes the control that matters. Trend 2: Attack chains are gaining autonomy The report charts a clear progression: September–November 2025: AI assists in orchestrating a campaign, as in the Anthropic-reported espionage case. April–June 2026: Full-chain autonomy is demonstrated in labs. July 2026: Real-world autonomous attacks of increasing complexity appear. Two frontier models, Mythos and GPT-5.5, completed a 32-step attack chain against an emulated enterprise with no human help. The test environment had no defenders. The UK AI Security Institute reached a similar finding independently. GPT-5.5 was the second model to solve its end-to-end corporate network attack simulation, a task AISI estimates would take a human about 20 hours. Open-weight models lag closed models at orchestrating attacks by about seven months. In the wild, early July 2026 saw the first documented automated ransomware extortion attack, named JADEPUFFER by Sysdig. Microsoft has seen AI-orchestrated intrusions that share elements with it. Initial access consistently came from internet-exposed services running known-vulnerable software. The report is measured on this point. Volumes of AI-orchestrated activity remain low. In most observed real-world operations, complex end-to-end intrusions still retain meaningful human direction. The Red Team's framing is the most useful one: AI doesn't create a new class of attack, it collapses the effort needed to find and exploit existing attack paths. Trend 3: AI has become an attack surface The report's second takeaway is that attackers are already targeting AI itself. The adoption numbers explain why. 88% of enterprises are experimenting with agents, and 82% of leaders plan broader rollouts within 12–18 months. The report cites industry projections of roughly 1.3 billion agents in production by 2028. What attackers are doing to AI systems: Four techniques accounted for roughly 90% of observed attack activity in telemetry from Azure AI workloads: malicious link injection (52%), jailbreaks (16%), tool and agent abuse (12%), and identity and credential abuse (10%). Memory poisoning: An attacker who can place content anywhere an agent reads can often place it where the agent remembers. Email is the dominant pathway the Red Team has observed. Human-in-the-loop isn't a complete control: Red team testing saw approval prompts ignored, or bypassed by a policy stored in poisoned memory. Developer tools turned against their owners: The "s1ngularity" malware hijacked locally installed AI command-line tools to hunt for secrets. It leaked about 2,000 secrets and 20,000 files across 225 victims. AI browsers: In daily use at more than 40% of organisations in Microsoft's dataset, they were the initial infection vector in incidents involving 57 malware families in a May 2026 analysis. Chat histories as a target: Malicious AI-assistant extensions reached nearly 900,000 installs and more than 20,000 enterprise tenants, harvesting AI chat histories that often contain code, architecture and credentials. The identity implications are direct. Each agent needs its own identity, not one borrowed from an employee, and a named human sponsor. The report also treats revocation latency, the time to cut off a compromised agent, as a measured outcome. Trend 4: The front door is still identity AI changes the speed and personalisation of social engineering. It hasn't changed where attacks start. Initial access in Defender Experts data: User execution: 30% Valid accounts: 20% Malicious copy-and-paste: 13% Phishing: 11% Together, user execution and valid accounts account for over half of observed activity. In incident response engagements, phishing as an initial access vector jumped from 7% to 23% year on year. The standout numbers: ClickFix: Defender saw user-pasted malicious commands on more than 1.1 million unique devices between February and early May 2026, roughly an 8x increase. AiTM: URLs accounted for 45% of the identified phishing techniques in the report’s breakdown. Separately, the share of AiTM phishing and token-theft attacks more than doubled during the first six months of 2026, although the report notes that their overall share remained comparatively small. Teams vishing: Weekly confirmed malicious volume increased 502% year on year. Per attempt, 48% of vishing calls kept the victim engaged for more than 20 seconds, and 30% lasted more than one minute. When a call succeeds, attackers can persuade the victim to install a remote-management tool and complete reconnaissance in two minutes or less. Business contact impersonation (BCI): The report introduces this term for conversations that start with a trusted-looking contact. Microsoft detected more than 46 million attacks in 12 months, with a 121% spike in April 2026. The share of first-contact messages that explicitly asked for money or documents fell from 17% to 3%. The first message is deliberately bland, like "Are you at your desk?" Password attacks declined 26% year on year, which the report describes as strong evidence that MFA and passkeys are forcing attackers to pivot. That pivot includes techniques such as fatigue attacks, AiTM phishing and token theft designed to circumvent conventional MFA. Trend 5: Attackers are inheriting trust rather than breaking in The report's headline theme is that risk is increasingly interconnected. Threats travel through the trusted relationships between identities, software supply chains, partners and cloud platforms. The numbers: Supply chain: Major open-source ecosystems saw attacks roughly every ten days, accelerating through Q2 2026. Shai-Hulud 2.0 affected 25,000 repositories. Megalodon backdoored 5,561 GitHub repositories in under six hours. Trusted signing: In May 2026, Microsoft disrupted Fox Tempest, a "malware-signing-as-a-service" operation that obtained genuine certificates for ransomware loaders and infostealers. Microsoft revoked over a thousand fraudulent code-signing certificates. Legitimate access: An unsanctioned remote monitoring tool is now one of the most reliable mid-stage indicators of a human-operated intrusion. Cloud identity: Cloud identity abuse was the most observed technique against critical infrastructure, representing 78% of the technique mix in Microsoft’s data for March to June 2026. Hybrid intrusions: The Storm-2885 case showed an attacker using a privileged cloud identity to close and downgrade security alerts. The report says identity governance and alert-handling workflows deserve Tier 0 treatment. Dwell time: DART data shows a statistically significant increase in dwell time year on year, meaning organisations took longer to detect intrusions. Ransomware shows the same ecosystem pattern. Qilin led leak-site data with 16.4% of victims, up 386%. Defender detections tell a different story, with Akira at 22%. Trend 6: Defence works best when it's connected The report is not only a catalogue of threats. The strongest defensive evidence: Disruption works. Following the March 2026 disruption, Tycoon2FA activity had fallen 95% from its November 2025 peak by June. Its share of CAPTCHA-gated phishing declined from 76% in December 2025 to 12% in June 2026. Correlation is the highest-leverage variable. The report argues that how well defenders share and correlate signals across endpoint, identity, cloud, email and network is one of the highest-leverage strategic variables under their control. AI is also changing defensive operations. The report describes AI-assisted triage, incident summarisation, hunting-query generation and detection engineering as ways to accelerate investigations and allow analysts to concentrate on higher-value work. Automated disruption can compress response time. The report says that when a correlated incident reaches a defined confidence threshold, defensive systems can automatically isolate devices, disable compromised accounts, revoke sessions or quarantine affected cloud resources. It also stresses the importance of appropriate governance and human oversight for high-risk AI actions. The report also recommends changing what leaders measure. It suggests moving from patches deployed to exposure reduced, detection coverage increased and time-to-mitigate compressed. What the MDDR recommends: ten security priorities The report starts its recommendations with the foundations: Secure by Design, Secure by Default and Secure Operations, applied through identity-centric Zero Trust. Microsoft says these keep the priorities below durable as AI adds more identities, applications, agents and dependencies to govern. Govern cyber risk at leadership level. Report identity exposure, patch latency, agent permissions, critical dependencies, dwell time and recovery readiness to the board. Defend identity as the primary control plane. Deploy passkeys and phishing-resistant MFA, reduce privilege and govern human and non-human identities. Invalidate and rotate tokens and session credentials quickly. Establish governance, visibility and data controls for AI. Keep visibility of approved AI tools, services and agents, and monitor for unauthorised or unmanaged use. Apply sensitivity-aware access and data protection to the data those systems can reach. Reduce exposure and edge risk. Continuously discover internet-facing assets and patch edge devices quickly. Centralise high-fidelity logging, enforce strong authentication and combine network visibility with threat intelligence. Focus on cross-signal correlation. Correlate endpoint, identity, cloud, application, email and network signals to detect intrusions earlier. Correlation also helps defenders anticipate attack paths and likely targets. Enact a continuous threat and vulnerability management model. Combine asset visibility, vulnerability identification, threat detection and AI-accelerated insight into a single continuous programme. Validate exposure through proactive testing. Run assume-breach exercises on cloud services, SaaS platforms, APIs and workflow automation. Prioritise them by where your highest-value data is stored, accessed or aggregated. Secure software dependencies. Harden software installation, keep a software inventory and validate provenance. Monitor packages and prepare for rapid containment. Participate in collective defence. Share actionable threat indicators and victim-informed intelligence with trusted partners and law enforcement. This supports coordinated tracking, investigation and disruption. Prepare for long-horizon technology transitions. Build a cryptographic inventory and advance post-quantum planning alongside AI governance. Read together, the numbers tell one story: the attacker’s workflow is becoming faster and less expensive. AI can shorten vulnerability discovery, exploitation, secret discovery and lateral movement, while organisations still face the slower operational work of testing, coordinating and deploying safe changes across complex environments. At the same time, agent identities can proliferate faster than organisations can inventory, govern and revoke them The defining question for boards today is not whether disruption will occur, but whether the organization can continue operating, contain harm, recover quickly, and maintain trust when it does. Source: Microsoft Digital Defense Report 2026, Version 1.1. Reporting period: July 2025–June 2026.
MC1457836 vs docs: Sentinel in unified RBAC with a service principal or GDAP. Which is it?
This is about the article https://learn.microsoft.com/en-us/defender-xdr/activate-defender-rbac, which states: Assigning permissions to a service principal or to a GDAP user group in Microsoft Sentinel isn’t supported in unified RBAC. If you need either capability, don’t activate Sentinel in unified RBAC yet. Continue using Azure RBAC for Microsoft Sentinel. Message center notice MC1457836 announces that Microsoft automatically enables unified RBAC on all tenants between late September and late December 2026, including Sentinel Reader, Responder and Contributor workspace permissions. These two statements cannot both be followed. The article tells affected customers not to activate Sentinel; MC1457836 activates it for them. This is not hypothetical for MSSPs. We operate a managed XDR service across roughly 25 customer tenants. Both unsupported capabilities are load-bearing in that model: A service principal holds Microsoft Sentinel Contributor through Azure Lighthouse and deploys analytics rules via ARM (Microsoft.OperationalInsights/workspaces/providers/alertRules). GDAP user groups are the standard delegated access route for partners. Could this be clarified: Does the service principal and GDAP limitation also apply to tenants activated through the MC1457836 automatic rollout, or does that path behave differently? If it does apply, what is the recommended action for tenants that are auto-activated before the limitation is lifted? Is deactivating the Sentinel workload after activation the supported mitigation, and is it expected to fully restore Azure RBAC behaviour for a service principal? Can a tenant be excluded from automatic Sentinel activation in advance, rather than only opting out afterwards? The article also warns that changing permissions in the Azure portal after Sentinel is active in unified RBAC may cause sync errors. For Lighthouse-delegated permissions, which are managed outside the customer tenant entirely, is that a supported configuration once Sentinel is active? A note in the “Before you begin” section describing how this limitation interacts with the automatic rollout would help partners decide whether to pre-emptively opt out.Integrating Proofpoint and Mimecast Email Security with Microsoft Sentinel
Microsoft Sentinel can ingest rich email security telemetry from Proofpoint and Mimecast to power advanced phishing detection. The Proofpoint On Demand (POD) Email Security and Proofpoint Targeted Attack Protection (TAP) connectors pull threat logs (quarantines, spam, phishing attempts) and user click data into Sentinel. Similarly, the Mimecast Secure Email Gateway connector ingests detailed mail flow and targeted-threat logs (attachment/URL scans, impersonation events). These integrations use Azure-hosted ingestion (via Logic Apps or Azure Functions) and the new Codeless Connector framework to call vendor APIs on a schedule. The result is a consolidated dataset in Sentinel’s Log Analytics, enabling correlated alerting and hunting across email, identity, and endpoint signals. Figure: Phishing emails are processed by Mimecast’s gateway and Proofpoint POD/TAP services. Security logs (delivery/quarantine events, malicious attachments/links, user clicks) flow into Microsoft Sentinel. In Sentinel, these mail signals are correlated with identity (Azure AD), endpoint (Defender) and network telemetry for end-to-end phishing detection. Proofpoint POD (Email Protection) Connector The Proofpoint POD connector ingests core email protection logs. It creates two tables, ProofpointPODMailLog_CL and ProofpointPODMessage_CL. These logs include per-message metadata (senders, recipients, subject, message size, timestamps), threat scores (spamScore, phishScore, malwareScore, impostorScore), and attachment details (number of attachments, names, hash values and sandbox verdicts). Quarantine actions are recorded (quarantine folder/rule) and malicious indicators (URL or file hash) and campaign IDs are tagged in the threatsInfoMap field. For example, each ProofpointPODMessage_CL record may carry a sender_s (sender email domain hashed), recipient list, subject, and any detected threat type (Phish/Malware/Spam/Impostor) with associated threat hash or URL. Deployment: Proofpoint POD uses Sentinel’s codeless connector (an Azure Function behind the scenes). You must provide Proofpoint API credentials (Cluster ID and API token) in the connector UI. The connector periodically calls the Proofpoint SIEM API to fetch new log events (typically in 1–2 hour batches). The data lands in the above tables. (Older custom logic-app approaches similarly parse JSON output from the /v2/siem/messages endpoints.) Proofpoint TAP (Targeted Attack Protection) Connector Proofpoint TAP provides user-click and message-delivery events. Its connector creates four tables: ProofPointTAPMessagesDeliveredV2_CL, ProofPointTAPMessagesBlockedV2_CL, ProofPointTAPClicksPermittedV2_CL, and ProofPointTAPClicksBlockedV2_CL. The message tables report emails with detected threats (URL or attachment defense) that were delivered or blocked by TAP. They include the same fields as POD (message GUID, sender, recipients, subject, threat campaign ID, scores, attachments info). The click tables log when users click on URLs: each record has the URL, click timestamp (clickTime), the user’s IP (clickIP), user-agent, the message GUID, and the threat ID/category. These fields allow you to see who clicked which malicious link and when. As the connector description notes, these logs give “visibility into Message and Click events in Microsoft Sentinel” for hunting. Deployment: The TAP connector also uses the codeless framework. You supply a TAP API service principal and secret (proofpoint SIEM API credentials) in the Sentinel content connector. The function app calls TAP’s /v2/siem/clicks/blocked, /permitted, /messages/blocked, and /delivered endpoints. The Proofpoint SIEM API limits queries to 1-hour windows and 7-day history, with no paging (all events in the interval are returned). (A Logic App approach could also be used, as shown in the Tech Community blog: one HTTP GET per event type and a JSON Parse before sending to Log Analytics.) Mimecast Secure Email Gateway Connector The Mimecast connector ingests the Secure Email Gateway (SEG) logs and targeted-threat (TTP) logs. Inbound, outbound and internal mail events from the Mimecast MTA (receipt, processing, delivery stages) are pulled via the API. Typical fields include the unique message ID (aCode), sender, recipient, subject, attachment count/names, and the policy actions or holds (e.g. spam quarantine). For example, the Mimecast “Process” log shows AttCnt, AttNames, and if the message was held (Hld) for review. Delivery logs include the success/failure and TLS details. In addition, Mimecast TTP logs are collected: URL Protect logs (when a user clicks a blocked URL) include the clicked URL (url), category (urlCategory), sender/recipient, and block reason. Impersonation Protect logs capture spoofing detections (e.g. if an internal name is impersonated), with fields like Sender, Recipient, Definition and Action (hold/quarantine). Attachment Protect logs record malicious file detections (filename, hash, threat type). Deployment: Like Proofpoint, Mimecast’s connector uses Azure Functions via the Sentinel content hub. You install the Mimecast solution, open the connector page, then enter Azure app credentials and Mimecast API keys (API Application ID/Key and Access/Secret for the service account). As shown in the deployment guide, you must provide the Azure Subscription, Resource Group, Log Analytics Workspace and the Azure Client (App) ID, Tenant ID and Object ID of the admin performing the setup. On the Mimecast side, you supply the API Base URL (regional), App ID/Secret and user Access/Secret. The connector creates a Function App that polls Mimecast’s SIEM APIs on a cron schedule (default every 30 minutes). You can optionally specify a start date for backfilling up to 7 days of logs. The default tables are MimecastSIEM_CL (for email flow logs) and MimecastDLP_CL (for DLP/TTP events), though custom names can be set. Ingestion Considerations Data Latency: All these connectors are pull-based and typically run on a schedule (often 30–60 minutes). For example, the Proofpoint POD docs note hourly log increments, and Mimecast logs are aggregated every 30 minutes. Expect a delay of up to an hour or more from event occurrence to Sentinel ingestion. Schema Nuances: The APIs often return nested arrays and optional fields. For instance, the Proofpoint blog warns that some JSON fields can be null or vary in type, so the parse schema should account for all possibilities. Similarly, Mimecast logs come in pipe-delimited or JSON format, with values sometimes empty (e.g. no attachments). In KQL, use tostring() or parse_json() on the raw _CL columns, and mv-expand on any multivalue fields (like message parts or threat lists). Table Names: Use the connector’s tables as listed. For Proofpoint: ProofpointPODMailLog_CL and ProofpointPODMessage_CL; for TAP: ProofPointTAPMessagesDeliveredV2_CL, ProofPointTAPMessagesBlockedV2_CL, ProofPointTAPClicksPermittedV2_CL, ProofPointTAPClicksBlockedV2_CL. For Mimecast SEG/TTP: MimecastSIEM_CL (seg logs) and MimecastDLP_CL (TTP logs). API Behavior: The Proofpoint TAP API has no paging. Be aware of timezones (Proofpoint uses UTC) and use the Sentinal ingestion TimeGenerated or event timestamp fields for binning. Detection Engineering and Correlation To detect phishing effectively, we correlate these email logs with identity, endpoint and intel data: Identity (Azure AD): Mail logs contain recipient addresses and (hashed) sender user parts. A common tactic is to correlate SMTP recipients or sender domains with Azure AD user records. For example, join TAP clicks by recipient to the user’s UPN. The Proofpoint logs also include the clicker’s IP (clickIP); we can match that to Azure AD sign-in logs or VPN logs to find which device/location clicked a malicious link. Likewise, anomalous Azure AD sign-ins (impossible travel, MFA failure) after a suspicious email can strengthen the case. Endpoints (Defender): Once a user clicks a bad link or opens a malicious attachment (captured in TAP or Mimecast logs), watch for follow-on behaviors. For instance, use Sentinel’s DeviceSecurityEvents or DeviceProcessEvents to see if that user’s machine launched unusual processes. The threatID or URL hash from email events can be looked up in Defender’s file data. Correlate by username (if available) or IP: if the email log shows a link click from IP X, see if any endpoint alerts or logon events occurred from X around the same time. As the Mimecast integration touts, this enables “correlation across Mimecast events, cloud, endpoint, and network data”. Threat Intelligence: Use Sentinel’s ThreatIntelligenceIndicator tables or Microsoft’s TI feeds to tag known bad URLs/domains in the email logs. For example, join ProofPointTAPClicksBlockedV2_CL on the clicked url against ThreatIntelligenceIndicator (type=URL) to automatically flag hits. Proofpoint’s logs already classify threats (malware/phish) and provide a threatID; one can enrich that with external intel (e.g. check if the hash appears in TI feeds). Mimecast’s URL logs include a urlCategory field, which can be mapped to known malicious categories. Automated playbooks can also pull Intel: e.g. use Sentinel’s TI REST API or Azure Sentinel watchlists containing phishing domains to annotate events. In summary, a robust detection strategy might look like: (1) Identify malicious email events (high phish scores, quarantines, URL clicks). (2) Correlate these events by user with Azure AD logs (did the user log in from a new IP after a phish click?). (3) Correlate with endpoint alerts (Defender found malware on that device). (4) Augment with threat intelligence lookups on URLs and attachments from the email logs. By linking the Proofpoint/Mimecast signals to identity and endpoint events, one can detect the full attack chain from email compromise to endpoint breach. KQL Query Here are representative Kusto queries for common phishing scenarios (adapt table/field names as needed): Malicious URL Click Detection: Identify users who clicked known-malicious URLs. For example, join TAP click logs to TI indicators:This flags any permitted click where the URL matches a known threat indicator. Alternatively, aggregate by domain: let TI = ThreatIntelligenceIndicator | where Active == true and _EntityType == "URL"; ProofPointTAPClicksPermittedV2_CL | where url_s != "" | project ClickTime=TimeGenerated, Recipient=recipient_s, URL=url_s, SenderIP=senderIP_s | join kind=inner TI on $left.URL == TI._Value | project ClickTime, Recipient, URL, Description=TI.Description This flags any permitted click where the URL matches a known threat indicator. Alternatively, aggregate by domain: ProofPointTAPClicksPermittedV2_CL | extend clickedDomain = extract(@"https?://([^/]+)", 1, url_s) | summarize ClickCount=count() by clickedDomain | where clickedDomain has "maliciousdomain.com" or clickedDomain has "phish.example.com" Quarantine Spike (Burst) Detection: Detect sudden spikes in quarantined messages. For example, using POD mail log:This finds hours with an unusually high number of held (quarantined) emails, which may indicate a phishing campaign. You could similarly use ProofPointTAPMessagesBlockedV2_CL. ProofpointPODMailLog_CL | where action_s == "Held" | summarize HeldCount=count() by bin(TimeGenerated, 1h) | order by TimeGenerated desc | where HeldCount > 100 Targeted User Phishing: Find if a specific user received multiple malicious emails. E.g., for user email address removed for privacy reasons:This lists recent phish attempts targeting Username. You might also join with TAP click logs to see if she clicked anything. ProofpointPODMessage_CL | where recipient has "email address removed for privacy reasons" | where array_length(threatsInfoMap) > 0 and threatsInfoMap_classification_s == "Phish" | project TimeGenerated, sender_s, subject_s, threat=threatsInfoMap_threat_s Campaign-Level Analysis: Group emails by Proofpoint campaign ID to see scope of each campaign:This shows each campaign ID with how many unique recipients were hit and one example subject. Combining TAP and POD tables on GUID_s or QID_s can further link click events back to the originating message/campaign. ProofpointPODMessage_CL | mv-expand threatsInfoMap | summarize Recipients=make_set(recipient), Count=dcount(recipient) by CampaignID=threatsInfoMap_campaignId_s | project CampaignID, RecipientCount=Count, Recipients, SampleSubject=any(subject_s) Each query can be refined (for instance, filtering only within a recent time window) and embedded in Sentinel Analytics rules or hunting. The key is using the connectors’ fields – URLs, sender/recipient addresses, campaign IDs – to pivot between email data and other security signals.
Is "Endpoint Security Policies" available to us? (error getting Intune policies)
Question We'd like to use Defender \ Endpoint Security Policies. Is that possible for my tenant's environment? Getting below error on "Defender \ Endpoint Security Policies" page "There seems to be an issue getting your Intune policies" Details of our environment Purpose of defender To protect our server fleet that's running outside of Azure Tenant GCC - Moderate Scoped Region Commercial Azure East US 2 Subscription Microsoft Defender for Servers Plan 1 (No other subscription, etc.) Defender Client OS Windows 2016, 2019, 2022 RHEL8, 9 (No desktops\laptops) Agents installed on each Windows and Linux server Defender is onboarded Arc is onboarded Configured Settings and Errors Defender \ Settings \ Configuration management \ Enforcement scope https://security.microsoft.com/securitysettings/endpoints/configuration_management2 Error at top of page "Intune is not configured to allow Microsoft Defender for Endpoint to manage security configuration settings." Use MDE to enforce security configuration settings from Intune Set to ON Enable configuration management Windows Server devices On tagged devices Windows Server Domain Controller devices On tagged devices Linux devices On tagged devices Security settings management for Microsoft Defender for Cloud onboarded devices. Set to ON Manage Security settings using Configuration Manager Set to OFF Defender \ Settings \ Configuration management \ Intune Permissions https://security.microsoft.com/securitysettings/endpoints/intune_permissions Getting error "Access needed You don't have the right permissions in AAD to view this information (in addition to those you already have in MDE). To adjust your permissions, go to the AAD portal." Defender \ Endpoint Security Policies https://security.microsoft.com/policy-inventory On main page, getting below error There seems to be an issue getting your Intune policies If I try to make a new policy There seems to be an issue loading the policy authoring wizard. Intune \ Endpoint security https://intune.microsoft.com/#view/Microsoft_Intune_Workflows/SecurityManagementMenu Getting Error You don't have access Intune roles | My permissions https://intune.microsoft.com/#view/Microsoft_Intune_DeviceSettings/RolesLandingMenuBlade/~/myPermissions You're an administrator with full permissions to all Microsoft Intune resources. Intune roles | Administrator Licensing https://intune.microsoft.com/#view/Microsoft_Intune_DeviceSettings/RolesLandingMenuBlade/~/administratorLicensing Allow admins without an Intune license to access Intune. Their scope of access is determined by the Intune roles you've assigned them. I've clicked the box "Allow access to unlicensed admins" Alternatives If Defender \ Endpoint Security Policies isn't available, as alternatives, I guess we could use SCCM Antimalware policies to manage Windows servers Deploying a central mdatp_managed.json to manage Linux servers However, it would be greatly preferred to use the Defender \ Endpoint Security Policies feature for Windows and Linux
Looking for a simple deployment guide
MS Learn is a great starting point, but it just doesn't seem to cover the steps needed to get up and running safely. I have concerns about adding or setting something that suddenly creates a vulnerability or exposure. Where is the installation guide that installs and configures the solution then tells you, "You are now protected". Do I really want to set my own policies? Why aren't the default set of rules good enough, safe enough. I can't have a solution that is so complicated I need to hire a team to manage it 24 hours a day. I am okay investigating an alert and helping a user solve a pop-up question. Why is every major corporation around the world required to re-invent the same or similar policies the company next door is creating to make this tool work? I want to onboard all of our Intune devices and monitor anything that CAN'T be stopped by default security measures. Just the fact that Sentinel appears to be changing as an embedded tool within Defender gives me hope that this will be getting closer to a more manageable tool. But that still seems a way off. I am ready to do the reading and research to get this set up but I am hoping for a guide that is specific enough to achieve a final result. Thank for understanding my challenges here.DefenderXDR "Preparing new space for data and connecting them" is stuck , and never finished !
Hello everyone, I am delivering SC-200 courses and on the lab environment of Skillable (or even free-tiers) when you have to "initiate" the data space for DefenderXDR, the process seems to be stuck .... never finished and we are "locked" in the page of a ...coffee cup and the phrase "Hang on. We are preparing new spaces for your data and connecting them. " does anyone else have same problem ? Any resolution , (I have already open a support ticket to Skillable support, but I haven't got resolution for over 1+day , and cannot open or continue the lab (for connecting or onboarding Microsoft Defender for Endpoint ) which is frustrating for the participants-students Thanks PanosOperational Notes on Microsoft Security Copilot Agents in Defender XDR and Microsoft Entra ID
Microsoft Security Copilot is now becoming more visible inside day-to-day security operations, especially through embedded experiences and agent-based workflows across Microsoft Defender XDR, Microsoft Entra ID, Microsoft Intune, and Microsoft Purview. Instead of looking at Security Copilot only as a standalone prompt interface, SOC and identity teams should also understand how Security Copilot agents are deployed, how they consume Security Compute Units, how they appear in operational workflows, and where activity can be monitored. This post summarizes practical observations from a security operations perspective, with a focus on Microsoft Defender XDR, Microsoft Entra ID, usage monitoring, and KQL-based activity review. Licensing & Capacity Units Requirements Requires eligible Microsoft security licensing, typically: Microsoft 365 E5 Microsoft 365 E7 Security Compute Units (SCUs) Security Copilot capacity is measured using Security Compute Units (SCUs). SCUs are billed based on provisioned capacity. Indicative pricing: $4 per Provisionied SCU/hour $6 per Overage SCU/hour Billing is calculated hourly, based on the amount of SCUs provisioned. Included Capacity Organizations with: 1,000 Microsoft 365 E5 licenses Receive: 400 included SCUs Included SCUs are shared across the tenant within a common capacity pool. Scaling SCU capacity can be scaled dynamically based on operational requirements and workload demand. Data Retention Security Copilot session and interaction data without active SCU-backed retention is typically retained for: 90 days Security Copilot Agents - Microsoft Defender This section outlines the Microsoft Security Copilot agents currently available in the Microsoft Defender portal. NameKey characteristics Security Alert Triage Agent (Preview) Manual setup from Defender portal Automatically creates Unified RBAC custom role Runs automatically when a user reports a suspicious email or when a new supported alert is generated, supported alert sources: MDI, MDC, MDO If an alert tuning rule is enabled, it will be automatically disabled when the agent is deployed. Creates and connects with agentic user account: Phishing Triage Agent (Security Copilot) Automatic alert assignment to SecurityCopilotAgentUser-db16fec3-f1fb-4632-843e-46d07408c584@<tenant-domain>Alert was assigned to Phishing Triage Agent (Security Copilot). Adds Tag Agent to the created Incidents Threat Hunting Agent Manual setup from Defender portal Automatically creates Unified RBAC custom role This agent runs manually. There isn't an automatic trigger. Creates and connects with agentic user account: Threat Hunting Agent (Security Copilot) Analyst Questions in natural language Generates and executed KQL queries in Advanced hunting Provides charts, dynamic follow-up questions and remediation actions recommendations No activity is identified from agent's identity during agent execution Threat Intelligence Briefing Agent Manual setup from Defender portal Provides automated TI briefing summary Configured from https://security.microsoft.com/securitysettings/defender/agent_configuration-threatintelligencebriefingagent Security Analyst Agent Manual setup from Defender portal Dynamic Threat Detection Agent (Preview) Automatically enabled always-on, runs continuously in the background Correlates: Alerts, Security events, Behavioral anomalies, TI signals Generates Alerts with Detection Source: Security Copilot The Alerts can be correlated with existing Multi-Stage Incidents No agentic user account identity is used by this agent Available free of charge during public preview, will begin consuming Security Compute Units (SCUs) once generally available (GA) Incidents handled by Security Alert Triage Agent: Alerts created by Dynamic Threat Detection Agent: Execution of Threat Hunting Agent: View agents in use: https://security.microsoft.com/security-copilot/agents View Unified RBAC custom roles: https://security.microsoft.com/mtp_roles View Security Copilot user identities in Microsoft Entra ID: Notes: CloudAppEvents activity logs only from the following agents: Phishing Triage Agent Conditional Access Optimization Agent Security Copilot Agents - Microsoft Entra ID Conditional Access Optimization Agent Usage Monitoring Sign-in to Security Copilot portal using Global Admin account and navigate to the following location: https://securitycopilot.microsoft.com/usage-monitoring Reference: https://learn.microsoft.com/en-us/copilot/security/manage-usage Logging Activity Copilot Agents Management: CloudAppEvents | where ActionType contains "CopilotAgent" | extend AgentName = RawEventData.AgentName | extend Workload = RawEventData.Workload | extend ResultStatus = RawEventData.ResultStatus | project TimeGenerated, ActionType, ResultStatus, AgentName, Application, Workload All Copilot Workload data: CloudAppEvents | extend Workload = RawEventData.Workload | where Workload == "Copilot" | summarize EventCount = count() by ActionType, AccountDisplayNameIdentity Attack Graph in Microsoft Sentinel
Identity is now one of the most important attack surfaces in cloud security. In many real-world incidents, attackers do not rely only on malware or network movement. Instead, they abuse identities, permissions, role assignments, group memberships, service principals, and misconfigured access paths to move from an initial compromise to high-value resources. This is why the new Identity Attack Graph in Microsoft Sentinel is an important capability. It helps security teams visualize how identities are connected to Azure resources and how an attacker could potentially move from one identity to another resource through permissions and relationships. What is the Identity Attack Graph? The Identity Attack Graph in Microsoft Sentinel provides a visual way to understand how identities, permissions, groups, and Azure resources are connected. Instead of manually checking multiple portals, logs, and role assignments, the graph helps analysts understand relationships such as: Which identities have access to specific Azure resources Which users or service principals are over-privileged Which groups provide indirect access to sensitive resources Which identities may have a path to critical assets What the potential blast radius of a compromised identity could be How attackers could move laterally through identity and permission relationships This is especially useful because identity risk is often not obvious when looking at a single user, group, or role assignment in isolation. The real risk usually appears when these relationships are connected together. A user may not directly have access to a sensitive resource, but the user may be a member of a group that has access to another resource, which then has permissions that create a path toward a high-value asset. The Identity Attack Graph helps expose these hidden relationships. Why this matters In many Azure environments, permissions grow over time. Users change roles, groups are reused, emergency access is granted, service principals are created, and temporary permissions are not always removed. As a result, organizations often end up with: Too many privileged identities Unused or stale permissions Service principals with excessive access Guest users with unnecessary permissions Groups that provide indirect access to sensitive resources Subscription-level roles that are broader than required Lack of visibility into who can reach critical assets Traditional investigation usually requires analysts to move between several places, including Microsoft Entra ID, Azure RBAC, Azure Activity logs, Sentinel queries, Defender XDR, and Azure Resource Graph. The Identity Attack Graph reduces this complexity by presenting identity relationships as a connected graph. This makes it easier to answer practical security questions such as: “What can this identity access?” “What happens if this user is compromised?” “Which identities have a path to critical resources?” “Which access path should we remediate first?” “Which permissions create the highest risk?” “Why does this identity have access to this asset?” Key use cases The feature can support several important identity security and cloud security scenarios. 1. Attack path discovery Security teams can use the graph to identify how an attacker could move from a compromised identity to a sensitive Azure resource. This is useful during both proactive assessments and active incident response. For example, if a user account is suspected to be compromised, the graph can help identify which resources may be reachable through that identity’s direct or indirect permissions. 2. Blast-radius analysis When an identity is compromised, one of the first questions is: What could the attacker access with this identity? The Identity Attack Graph can help analysts understand the potential impact of a compromised user, group, service principal, or managed identity. This can help with containment, prioritization, and communication with stakeholders. 3. Over-privileged identity detection The graph can help identify identities that have more permissions than they need. Include: Users with Owner or Contributor access at subscription level Service principals with broad permissions Guest users with privileged access Groups that grant access to sensitive resources Identities that have access to multiple critical assets This is useful for enforcing least privilege and reducing identity attack surface. 4. Privileged access review IAM and cloud security teams can use the graph to support access reviews. Instead of only reviewing a list of role assignments, teams can understand the real impact of those permissions. This helps answer: Is this role assignment still required? Does this group create unnecessary risk? Does this identity have access to critical resources? Is this access direct or inherited? Is this path expected or suspicious? 5. Incident response and threat hunting For SOC teams, the graph can support investigations involving: Suspicious sign-ins Compromised users Privilege escalation Suspicious role assignments Lateral movement Service principal abuse Unusual access to sensitive resources The graph does not replace logs or hunting queries, but it gives analysts a faster way to understand relationships and prioritize what to investigate next. Important prerequisites and setup notes During my evaluation, there were a few important setup requirements that should be clearly highlighted. Microsoft Sentinel permissions The environment must already be onboarded to Microsoft Sentinel, and the user testing or configuring the feature must have the appropriate Microsoft Sentinel permissions. The documented role requirement includes Microsoft Sentinel Contributor. However, in my experience, this is not always enough for the full onboarding and validation experience. Subscription-level Owner permission One important prerequisite that should be clearly mentioned is that Owner permissions at the Azure subscription level may be required. This is especially important during onboarding and activation, because the graph depends on access to Azure resource and permission relationships. If the user does not have sufficient subscription-level permissions, some setup steps or visibility into resources and relationships may not work as expected. Recommended permission note: In addition to Microsoft Sentinel permissions, ensure that the user configuring the preview has Owner permissions at the subscription level for the subscriptions that should be represented in the graph. This should be made very clear in the onboarding documentation to avoid confusion during deployment. Required data connector: Azure Resource Graph Another very important setup step is the Azure Resource Graph data connector. The Azure Resource Graph connector must be: Installed manually Activated manually Connected to the relevant Sentinel workspace This is a key point. The connector is not automatically enabled just because the Identity Attack Graph feature is available. Without this connector, Sentinel may not have the required Azure resource relationship data needed to build a useful graph. Why Azure Resource Graph is important Azure Resource Graph provides visibility across Azure resources, subscriptions, and relationships. For an identity attack graph, this data is essential. The graph needs to understand not only identities, but also the resources those identities can reach. This may include: Subscriptions Resource groups Storage accounts Key Vaults Virtual machines Managed identities Role assignments Resource relationships Resource hierarchy Critical assets Without Azure Resource Graph data, the attack graph may not provide the full picture of how identities connect to Azure resources. For this reason, I believe the onboarding instructions should explicitly state: The Azure Resource Graph data connector must be manually installed and activated before using the Identity Attack Graph. Recommended onboarding checklist Before using the Identity Attack Graph, I would recommend validating the following: Requirement Recommendation Microsoft Sentinel workspace Ensure the workspace is active and accessible Sentinel role Microsoft Sentinel Contributor or equivalent access Subscription permissions Owner permissions at subscription level Azure Resource Graph connector Manually install and activate the connector Azure RBAC visibility Ensure access to relevant role assignments Microsoft Entra ID visibility Ensure identity and group data is available Resource visibility Validate that relevant subscriptions and resources are visible Data freshness Allow enough time for data collection and graph population This checklist can help avoid issues where the feature appears available but does not show the expected relationships. How the Identity Attack Graph improves investigation Before using a graph-based approach, an analyst often needs to manually collect and correlate data from multiple sources. A typical investigation may include: Checking the user in Microsoft Entra ID Reviewing group memberships Reviewing Azure RBAC assignments Checking subscription-level access Looking at resource-level permissions Reviewing PIM activations Searching Sentinel logs Running KQL queries Checking Azure Activity logs Validating access with cloud or IAM teams This process can be time-consuming. The Identity Attack Graph helps reduce this effort by showing relationships visually. This allows the analyst to understand the possible path faster and decide where to focus. For example, instead of manually asking: “Does this user have access to this resource through any group, role, or inherited permission?” The graph can help show the relationship directly. This is valuable because many risky permissions are indirect. The user may not have direct access, but may inherit access through a group, role assignment, nested relationship, or service principal path. Where validation is still needed Although the graph provides strong visibility, I would still validate findings before taking remediation action. This is especially important because removing access can affect business operations or production systems. I would still validate with: Microsoft Sentinel KQL queries Microsoft Entra sign-in logs Microsoft Entra audit logs Azure Activity logs Azure RBAC role assignments PIM activation history Defender XDR signals Defender for Cloud recommendations Azure Resource Graph queries IAM team input Cloud platform team input Application owner confirmation The graph is very useful for discovery and prioritization, but final remediation decisions should still be validated. GQL and graph-based investigation One of the interesting aspects of this feature is the use of graph-based thinking. Security teams are already familiar with query languages such as KQL for log analytics. However, graph investigation is different. KQL is excellent for searching and analyzing events over time, such as sign-ins, alerts, audit logs, and activity logs. Graph Query Language, or GQL, is designed for querying connected data. Instead of only asking what happened at a specific time, graph queries help answer how entities are connected. In identity security, this is very powerful because the risk often exists in the relationship between objects. Graph entities include: Users Groups Service principals Managed identities Roles Subscriptions Resource groups Azure resources Permissions Sessions Attack paths Graph relationships include: User is member of group Group has role assignment Identity has access to resource Service principal owns application Managed identity can access Key Vault User can escalate privilege Identity can reach critical asset This allows analysts to ask more relationship-focused questions, such as: Which identities can reach this resource? What is the shortest path from this user to a critical asset? Which groups create privileged access? Which service principals have paths to sensitive resources? Which identities have indirect access through nested relationships? Which attack paths include subscription Owner or Contributor permissions? KQL vs GQL: why both are useful KQL and GQL serve different but complementary purposes. Area KQL GQL / Graph Querying Main purpose Analyze logs and events Analyze relationships and paths Best for Time-based investigation Connected identity/resource investigation question “Did this user sign in from a risky location?” “What resources can this user reach?” Data model Tables Nodes and edges Common use Detection, hunting, analytics Attack path discovery, relationship mapping Strength Event correlation Path discovery In practice, security teams need both. KQL can identify a suspicious sign-in. The Identity Attack Graph can show what the compromised identity could access. KQL can then be used again to validate whether the attacker interacted with those resources. This creates a strong workflow between event-based detection and relationship-based investigation. Graph investigation scenarios The following are conceptual are the types of graph questions that would be useful in identity attack path analysis. Find paths from a user to critical resources A useful graph query would help answer: Show me all paths from this user to critical Azure resources. This could help determine whether a compromised identity has a direct or indirect route to sensitive assets. Find identities with paths to Key Vaults Key Vaults often contain secrets, certificates, and keys. A graph query could help identify: Which users, groups, service principals, or managed identities have a path to Key Vault resources? This would be useful for prioritizing access review and remediation. Find subscription-level privileged identities Subscription-level roles are high-impact because they can provide broad access. A graph query could help find: Which identities have Owner or Contributor access at subscription level? This is especially important because subscription-level permissions can create wide attack paths. Find indirect access through groups Many access paths are created through group membership. A graph query could help answer: Which users have access to this resource through group membership? This can help IAM teams clean up excessive or unnecessary group-based access. Find service principals with broad access Service principals are often used for automation and applications, but they can become high-risk if over-privileged. A useful query would identify: Which service principals have broad access to subscriptions or critical resources? This is important because service principal compromise can lead to significant impact. How GQL can improve analyst workflows Adding strong GQL support to the graph explorer would make the feature more powerful for advanced users. You could use graph queries to: Search for specific paths Filter by identity type Filter by role Filter by resource type Find shortest paths Find high-risk paths Exclude known approved paths Focus on critical assets Query only privileged relationships Identify unexpected permission chains This would help both SOC analysts and cloud security engineers move from visual exploration to repeatable analysis. A SOC analyst may want a quick visual graph during an incident, while a cloud security engineer
