Unexpected button behaviour when using the prompt=create parameter in Entra External ID user flows
Hi, In a recent workload, I'm assisting a client to implement Entra External ID for streamlined authorization as well as single sign-on for associated registered external facing third-party applications for external customer users through their Entra External ID identity, as well as assisting the client with auth branding and other UI customizations, and preparing Entra ID federation custom OIDC providers and configuration for enabling SSO with organizational internal and remote work- and school accounts etc. The sign-up / create account experience is of importance to the client, as a rather substantial amount of users are expected to sign-up via self-service sign-up following having received an invite via another app. To ensure that the user lands on the create account view, in order to minimize the number of steps and actions the users need to take to get there, the prompt=create parameter is used to pre-select the sign-up/register experience in the user flows UI. Having stress-tested the user flows and experience recently, we noticed that in a specific scenario, some UI elements behave in a manner that could be described as unexpected, and even though a workaround has mitigated it to some extent, the behaviour of some elements could probably be improved a bit to ensure an even more consistent user experience in the built-in user flows. Specifically, if the user flow is invoked with the prompt parameter set, the user lands on the create account screen as expected, however, unless custom CSS modification is applied, the Back button that would typically be there, as if having arrived there from the initial sign-in screen where it’s also displayed. If pressing the Back button displayed on the Create account view when having navigated there with the prompt=create set in the /authorize request, pressing the button seemingly doesn’t have any impact or result in any action, one can click it, but nothing happens. I'd suspect it's perhaps a "remnant" from if the flow is invoked without any prompt parameter set, or with prompt=login set, but when prompt=create is set, there is no state/page history in the UI to navigate back to, as no previous page has been displayed or rendered yet and added to the history, and the Back button click thus doesn’t have any effect. In general, I think buttons that don't have any tangible action should not be displayed, also, if prompt=login is set, the Back button isn't shown then either on the very initial user flow view shown then, so it would seem the built-in user flows actually already follow such approach in fact, but not when the prompt=create has been set, thus causing some inconsistency in the UI that users could notice unless custom CSS styling is applied. The expected behaviour for our business case would be that if prompt=create is set, then, similar to the prompt=login, no Back button should be displayed, or, if shown, it should result in some navigation (perhaps something like javascript.go(-1), but that could/would be dependent on from where the user arrived, e.g., going back a step in the browser history won't work if the user clicked a link from an invite email ) and preferably not be non-actionable. Furthermore, on the topic of the Back button and its behaviour when the prompt=create parameter is set, there is another case at which we observed some unexpected button behaviour as well, that could probably benefit from some attention. At a specific second scenario, it seems that the Continue button is displayed without providing any action, and when clicking the Back button then, clicking it seemingly resets the "create account", state likely set by the prompt=create initially, and instead switches the flow back to the sign-in state, which can at least affect the display text of some subsequent buttons shown in later steps in the UI. The user arrives at a built-in Entra External ID user flow /authorize endpoint, with the prompt=create query parameter set The user triggers the OTP challenge by entering an email address to verify the email address The user receives the OTP code but enters it incorrectly, i.e., doesn't copy the full code length of eight digits, and instead enters/pastes six of the eight code digits. The UI then shows an error message that the code could not be used/validated, and the email address field is displayed again with the email address that was used for the verification attempt prefilled. If the user clicks the Continue button at this stage, nothing happens. If the user clicks the Back button instead (given that it’s not hidden), it shows the same view one more time, with the Back and Continue buttons at the bottom. However, if the user clicks the Continue button this time, it works and a new code is sent. On the next screen, where the newly sent code can be entered, instead of a button named "Continue", it will now instead show a button with the text "Sign-in" (it would seem like the create account state has gotten lost somewhere along the way at this point, perhaps when the back button in the step 5 above was pressed). If one omits the prompt=create parameter, or when using prompt=login, things seem to work fine, the above occurs when the prompt=create is set. The reason why is the prompt=create is used is due to a business requirement to try to minimize the steps, especially in conjunction with the registration/sign-up, as far a possible when signing up. We have opted for the built-in user flows, not the self-hosted native authentication UI pages, for this project, and then as I understand it, it is the prompt=create parameter that can/should be used to enable the user to land directly on the registration page without having to navigate there manually via the sign-up link, that is otherwise shown on the initial sign-in page. It would thus be great if the prompt=create parameter could have some attention (or perhaps if a dedicated sign-up user flow type could be added potentially, even though that would perhaps warrant some update to the user flow app linking as well, as my understanding is that one user flow can be linked to one app registration at a time currently) to avoid that some buttons become unresponsive when the parameter is used, or falls back to sign-in, to improve the built-in user flows further, as the prompt=create fulfils the business requirement well otherwise. If there would be any further/follow-up questions on the above, e.g., to clarify the requirement, or further explain the reproduction steps and behaviour observed, or anything else, please tell, and I'll ensure to get back as soon as possible. Also, would someone have some input on potential other/additional ways to pre-select the create account/sign-up experience, that would naturally be much appreciated as well, thanks! Regards KristofferAuthenticating AWS Workloads to Azure Functions using Workload Identity Federation
Step-by-step guide to configuring Workload Identity Federation between AWS and Azure, enabling service-to-service authentication where AWS workloads can securely call Azure Functions using token-based access instead of stored credentials.
Microsoft Leads a New Era of Software Supply Chain Transparency
Today, Microsoft announces the general availability of Microsoft’s Signing Transparency (MST) – a first-of-its-kind capability that brings unprecedented visibility and trust to our software supply chain. With this release, Microsoft is leading the industry by recording the build of critical cloud services into a publicly readable and verifiable SCITT standard (Supply Chain Integrity, Transparency, and Trust) compliant blockchain ledger. This means every production software build for in scope services like Azure Attestation and Azure Managed HSM (Hardware Security Module), Azure confidential ledger, Microsoft Signing Transparency itself (and others over time) – is now logged in an immutable, tamper-evident record. Only builds that are in the MST ledger are deployed to production; this gives customers confidence that the supply chain for these critical services can be audited at anytime. Notably, the MST ledger is fully open source and built to align with the emerging IETF SCITT standard. By embracing SCITT’s principles and open protocols, Microsoft ensures that MST not only secures our own ecosystem but also contributes to a broader industry movement toward standardized supply chain transparency. The open-source MST ledger serves as a verifiable trust anchor that any organization or researcher can inspect, audit, or even integrate with their own tooling. MST itself meets the highest levels of transparency, backed by a tamper-proof confidential ledger, open-source, and independently verified. Specifically, we are making the foundation of our trust model transparent and accessible to everyone – reinforcing that trust must be earned through proof, not just promises. This launch marks a major milestone in our commitment to Zero Trust principles, extending “never trust, always verify” all the way into the build itself. Building on a public preview introduced late last year, MST’s general availability delivers verifiable transparency at the software level. It transforms traditional code signing with an additive trust layer that is accessible via an open verification model. Every new software update is accompanied by a publicly auditable proof of integrity, enabling security teams to proactively confirm that each update is authentic and unaltered. To help organizations get the most out of this capability, we are also introducing a free tool to explore the contents – Ledger Explorer – an offline tool that allows security teams to examine MST ledger entries, verify cryptographic proofs, and even validate the ledger’s integrity independently. This tool, combined with MST’s open design, ensures that every Microsoft customer – and the broader community – can hold us accountable in real time for the software we run on their behalf. Key Benefits of Microsoft’s Signing Transparency (MST) Verified Code Integrity – Every software release is cryptographically logged in MST’s ledgers. This makes each build tamper-evident and traceable. If an attacker attempts to inject malicious code or sign an unauthorized update, it will be evident through the well-defined validation step built into the SCITT standard. Organizations gain the assurance that code integrity can be independently confirmed at any time. Independent Verification & Zero Trust – MST enables customers and auditors to verify software authenticity on their own, without having to solely rely on vendor attestations. For each update, Microsoft provides a transparency “receipt” (proof of logging) that you can use to prove the update was officially published and unaltered. This fosters a “don’t just trust, verify” approach, empowering security teams to double-check everything running in their environment aligns with what Microsoft intended. Audit-Trail & Compliance – The transparency ledger creates a permanent, auditable timeline of code deployments. Every entry is a record of what was released and when, backed by cryptographic proofs. This simplifies compliance reporting and accelerates forensic analysis. In the event of an incident, you can quickly audit the ledger to see if any unexpected code was introduced. For highly regulated industries, MST offers concrete evidence of software integrity and policy compliance over time. Leadership & Open Standards – We are delivering real transparency now, encouraging a future where all critical software is released with verifiable integrity. MST’s open source implementation and SCITT-compliant design exemplify our commitment to openness and collaboration. We believe widespread adoption of these standards will strengthen supply chain security for everyone, making trust verification a universal practice. Next Steps Microsoft’s Signing Transparency is more than a new security feature and shapes the advances in trust technology. As threats grow more sophisticated, we must evolve the way we assure our customers about the software they depend on. With MST now generally available, we are leading by example: proving that it is possible to open up the traditionally opaque process of software deployment and turn it into a source of strength and trust, i.e., empowering each person with verifiable transparency. We invite the industry to join us on this journey and get started by reading the documentation and exploring Ledger Explorer today! Together, by embracing transparency and open standards, we can turn “trust but verify” from a slogan into an everyday reality for digital infrastructure.Level up your Azure Network Security Skills with our Upcoming Webinar Series
As network and application-layer threats continue to evolve, security and infrastructure teams need more than product knowledge. They need practical, scenario-driven guidance they can apply to real workloads. To support that, the Azure Network Security team is hosting a series of upcoming technical webinars covering the capabilities our customers rely on every day: Azure Web Application Firewall (WAF), Azure Firewall, Azure DDoS Protection and Azure Bastion. Each session is focused on demos, the latest enhancements, and the design and operational decisions you face when securing modern Azure environments. Whether you are protecting customer-facing web applications, hardening east-west and egress traffic, or securing remote administrative access at scale, there is a session in this lineup for you. These webinars are ideal for Security Architects and Engineers, Network and Infrastructure teams, SOC Analysts, Cloud Platform Owners, Partner Technical Consultants, and any practitioner responsible for the security posture of workloads running on Azure. Below is the schedule of the upcoming live deliveries. Upcoming Events Azure WAF Layer 7 DDoS defense in practice Date and time: Thursday, June 18, 2026, at 8am PST View event details and join As web applications become primary targets for sophisticated application-layer attacks, Azure Web Application Firewall continues to evolve to meet the needs of modern application security teams facing volumetric and targeted application-layer threats. In this webinar, we will explore how Azure WAF enables a layered, adaptive approach to application-layer DDoS mitigation, helping organizations detect and block malicious request patterns through intelligent inspection, control traffic flow to prevent resource exhaustion from abusive sources, progressively challenge suspicious clients to verify legitimacy without disrupting real users, and combine multiple defense mechanisms into a cohesive mitigation strategy that adapts to evolving attack techniques. Whether you're securing customer-facing web apps or business-critical services, this session will equip you with practical approaches to building resilient application-layer defenses on Azure. Azure Firewall IDPS Detections and Sentinel Integration Date and time: Thursday, July 9, 2026, at 8am PST View event details and join As network threats grow in complexity, organizations need visibility that extends beyond simple traffic filtering into intelligent detection and unified investigation workflows. Azure Firewall's Intrusion Detection and Prevention capabilities continue to evolve to meet the needs of modern security operations teams facing advanced lateral movement, exploitation attempts, and command-and-control activity. In this webinar, we will explore how Azure Firewall identifies malicious network patterns in real time, how detection signals flow seamlessly into Microsoft Sentinel to enrich the broader security narrative, and how security teams can correlate firewall intelligence with other data sources to accelerate threat hunting, streamline incident response, and build a more connected and actionable view of their network security posture. What's New in Azure Bastion Date and time: Thursday, July 23, 2026, at 8am PST View event details and join Secure remote access to cloud workloads remains a critical requirement as organizations scale their Azure environments and adapt to evolving operational demands. Azure Bastion continues to evolve to meet the needs of modern infrastructure teams seeking seamless, browser-based connectivity without exposing virtual machines to the public internet. In this webinar, we'll explore the latest enhancements to Azure Bastion covering new capabilities that improve connectivity options, streamline the administrative experience, expand protocol and session support, and strengthen the overall security posture of remote access workflows. Whether you're managing a handful of VMs or operating at enterprise scale, this session will bring you up to speed on what's new and how these improvements can simplify and secure your day-to-day operations. What's New in Azure Firewall Date and time: Thursday, August 6, 2026, at 8am PST View event details and join As network architectures grow more distributed and threat landscapes more dynamic, organizations need a cloud-native firewall that keeps pace with both modern workload patterns and adversary techniques. Azure Firewall continues to evolve to meet the needs of network and security teams managing hybrid environments, multi-region deployments, and increasingly complex east-west and north-south traffic flows. In this webinar, we will explore the latest enhancements to Azure Firewall covering new policy and rule management capabilities, improvements that expand protocol and traffic inspection coverage, and deeper integrations across the Azure security ecosystem to streamline operations. Whether you are standardizing perimeter protection across a global Azure footprint or modernizing segmentation for business-critical workloads, this session will bring you up to speed on what is new and how these improvements can simplify and strengthen your day-to-day network security operations. What's New in Azure Web Application Firewall Date and time: Thursday, August 27, 2026, at 8am PST View event details and join Web applications remain primary entry points for attackers, and organizations need a Web Application Firewall that adapts as quickly as the threats targeting their workloads. Azure Web Application Firewall continues to evolve to meet the needs of modern application security teams defending against an expanding mix of OWASP-class attacks, automated abuse, and business logic threats across diverse hosting models. In this webinar, we will explore the latest enhancements to Azure WAF. We will cover new detection and rule capabilities that improve protection accuracy, tuning and exclusion improvements that reduce false positives without weakening coverage, and expanded visibility and analytics that accelerate investigation. Whether you are securing customer-facing web apps or managing WAF policies at scale, this session will bring you up to speed on what's new and how these improvements can simplify and strengthen your application protection strategy Past Recordings: View additional past webinars from Azure Network Security on Microsoft Security Community YouTube. 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Join the Microsoft Security Community LinkedIn and the Microsoft Entra Community LinkedInDetecting AI agents and non-human identities in Microsoft Sentinel: the classic-agent blind spot
Build 2026 made the direction official. The industry is moving from the app era into the agent era, and Microsoft spent a real share of the keynote on securing agents across their lifecycle, from discovering what is exploitable to governing what is running in production. On the identity side the centerpiece is Microsoft Entra Agent ID, now generally available, which gives AI agents first-class identities and extends Conditional Access, Identity Protection, and full audit logging to them. That is good news for agents you build the new way. It is not the whole picture, and the gap is where most SOCs will get hurt first. Modern agents are covered. Classic agents are not. Entra Agent ID draws a hard line between two kinds of agent. Modern agents are created through the Agent ID platform, each backed by an agent identity blueprint. They carry a proper Agent ID, a full audit trail, and the complete set of governance capabilities, including Identity Protection for Agents, which establishes a baseline for an agent's normal activity and flags anomalies automatically. Classic agents are everything that came before, or that gets built outside the platform: AI agents implemented as ordinary service principals or app registrations, for example Copilot Studio agents created before Agent ID was enabled, or any home-grown automation calling Graph with client credentials. In the Entra agent registry they appear with "Has Agent ID: No," and that flag matters, because the Agent ID protections apply to identities that actually hold an Agent ID. Classic agents sit outside Identity Protection for Agents and Conditional Access for Agents. Here is the uncomfortable part. The non-human identities you already run, the service principals behind your pipelines, your integrations, your scripts, your pre-platform Copilot Studio bots, are almost all classic agents. They tend to outnumber your human accounts, they have no MFA in any meaningful sense, and a credential added to one does not show up in the Azure portal. The new platform protections do not reach them. Until you migrate them, the only place you get detection coverage on that population is your SIEM. So this is the job Sentinel does that Agent ID does not: detect risky behavior on the classic, service-principal-backed agents that the platform cannot yet protect. The telemetry you have, and the one switch people forget Three tables carry most of the signal. AADServicePrincipalSignInLogs records service principal authentications, the client-credentials sign-ins your agents and automation use. No user, no MFA, just an app proving it holds a secret or certificate. AADManagedIdentitySignInLogs does the same for managed identities. AuditLogs records directory changes, including the one that matters most for persistence: a new credential added to an application or service principal. One practical warning before any of this works. Service principal and managed identity sign-in logs are not streamed by default. You have to enable those categories explicitly in the Entra diagnostic settings feeding your workspace. Plenty of teams write the detection, never check, and never notice the table is empty. Verify that first. Detection 1: a new credential on a service principal or app Adding a secret or certificate to an existing service principal is one of the cleanest persistence techniques in a Microsoft cloud. The attacker compromises a privileged user or app, drops a fresh credential on a service principal that already holds useful Graph permissions, and now has access that survives password resets and session revocation. It maps to MITRE T1098.001, Account Manipulation: Additional Cloud Credentials. For a classic agent it is especially nasty, because there is no Identity Protection baseline watching it. // Detection 1: new secret or certificate added to an application or service principal // MITRE T1098.001 - Account Manipulation: Additional Cloud Credentials AuditLogs | where OperationName has_any ("Add service principal", "Certificates and secrets management") | where Result =~ "success" | extend Initiator = coalesce( tostring(InitiatedBy.user.userPrincipalName), tostring(InitiatedBy.app.displayName)) | extend InitiatorIp = tostring(InitiatedBy.user.ipAddress) | mv-apply Target = TargetResources on ( where Target.type =~ "Application" | extend TargetName = tostring(Target.displayName), TargetId = tostring(Target.id), KeyChanges = Target.modifiedProperties ) | mv-apply Prop = KeyChanges on ( where tostring(Prop.displayName) =~ "KeyDescription" | extend NewKeys = parse_json(tostring(Prop.newValue)), OldKeys = parse_json(tostring(Prop.oldValue)) ) | extend AddedKeys = set_difference(NewKeys, OldKeys) | where array_length(AddedKeys) > 0 | project TimeGenerated, Initiator, InitiatorIp, TargetName, TargetId, AddedKeys | order by TimeGenerated desc The operation filter catches the three shapes this event takes in the log: "Add service principal," "Add service principal credentials," and "Update application - Certificates and secrets management." The modifiedProperties parsing isolates the KeyDescription change, and set_difference confirms a key was actually added rather than removed, so rotating out an old credential does not, on its own, fire the rule. False positives come from legitimate rotation and from automation that provisions app credentials (CI/CD, infrastructure as code). The initiator is the discriminant. A credential added by your deployment pipeline's service account at the usual time is routine. The same change initiated by an interactive admin out of hours, or by an account that never normally touches app credentials, is what you want to surface. Allow-list the expected initiators, not the targets. Detection 2: a classic agent signing in from a first-seen IP A service principal that has only ever authenticated from your Azure regions and suddenly signs in from somewhere new is a strong signal that its credential has been lifted and is being used elsewhere. Service principals have stable, boring network behavior, which makes a first-seen IP a far cleaner indicator for them than it is for roaming human users. This is the behavioral baseline Identity Protection gives you for free on modern agents, rebuilt in KQL for the classic ones it ignores. MITRE T1078.004, Valid Accounts: Cloud Accounts. // Detection 2: classic-agent service principal signing in from a previously unseen IP // MITRE T1078.004 - Valid Accounts: Cloud Accounts let baseline = 14d; let detection = 1d; let KnownIPs = AADServicePrincipalSignInLogs | where TimeGenerated between (ago(baseline + detection) .. ago(detection)) | where tostring(ResultType) == "0" | summarize KnownIPSet = make_set(IPAddress) by AppId; AADServicePrincipalSignInLogs | where TimeGenerated > ago(detection) | where tostring(ResultType) == "0" | lookup kind=leftouter KnownIPs on AppId | where set_has_element(KnownIPSet, IPAddress) == false | summarize FirstSeen = min(TimeGenerated), Resources = make_set(ResourceDisplayName, 10) by ServicePrincipalName, AppId, IPAddress | order by FirstSeen desc The query builds a per-application baseline of source IPs over the previous two weeks, then flags any successful sign-in today from an address outside that set. Two tuning notes. Brand-new service principals have no baseline, so they surface on first use. That is usually worth seeing once, but you can exclude AppIds younger than the baseline window if it gets noisy. And if your agents egress through shifting cloud IP ranges, widen the comparison from an exact IP to the autonomous system number or a known-range allow-list, otherwise you will chase your own infrastructure. This complements Agent ID, it does not replace it! The endgame is not to run these rules forever. It is to shrink the population they apply to. Inventory your tenant for agents marked "Has Agent ID: No," prioritize the ones holding sensitive Graph permissions, and migrate them onto the Agent ID platform, where Identity Protection and Conditional Access take over the baselining you are doing here by hand. Microsoft has signaled a migration path from classic to modern agents. Treat these two detections as the coverage you need in the meantime, and as a permanent safety net for anything that never makes the move. If you do one thing this week: enable the service principal sign-in log category, deploy detection 1, and pull a list of every service principal that had a credential added in the last 90 days. That list alone tends to be more interesting than people expect. Cheers, MarcelState 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.Sentinel Foundry - MCP Server (Github Community Release)
I’ve been cooking something that a lot of people in SOC have been struggling with — especially on the engineering side of Microsoft Sentinel. Thanks to the Microsoft Security team for shaping the capabilities of Sentinel even better with Sentinel Data Lake & Modern SecOps. Today’s the day I can finally share it. Note: This is not an official Microsoft product, but it is designed to make the Sentinel Build even better (complement) with much more intelligence. 🚀 Sentinel Foundry is now in public preview with 43 tools. (Sentinel Foundry - MCP Server) It’s an MCP server built to act like the brain of a strong Sentinel engineer — helping make building, improving, and operating Sentinel far more practical, faster, and honestly more enjoyable. For a lot of teams, the challenge is not understanding what Sentinel can do. The hard part is the engineering work around it: -> Deciding what data should actually be ingested -> Building a clean, scalable Sentinel foundation -> Writing useful detections instead of noisy ones -> Balancing security value with cost -> Turning ideas into deployable engineering outputs That is exactly why I built Sentinel Foundry to help communities grow stronger. It helps with the real engineering tasks behind Sentinel — from architecture thinking to detection design, deployment planning, ingestion strategy, automation ideas, and many of the workflows outlined in the GitHub project. How does it work? Here’s one of the flagship prompts I ran with it: “Give me a complete security posture report for our workspace. Score each pillar and tell me what to prioritise.” And within seconds, it produced a structured engineering blueprint that would normally take a lot longer to pull together manually. You can see the example prompts here in what it can do: https://github.com/prabhukiranveesam/Sentinel-Foundry#what-can-it-do I want building Sentinel to feel less like repetitive engineering overhead — and more like real security engineering that is fast, creative, and enjoyable. If you work with Sentinel as a SOC L2 analyst, engineer, detection engineer, consultant, or architect, I’d genuinely love for you to try it and tell me what you think. 🔗 Public Preview: https://github.com/prabhukiranveesam/Sentinel-Foundry This is just the start of an AI era — and I’m excited to keep shaping it with more powerful features over the coming days. This is very easy to set up and will be available to all of you at no cost during this month as part of the public preview, and your feedback is extremely valuable to shape this as a powerful solution.Extending Sentinel Data Integration: Azure Blob Storage Support for CCF Connectors
As organizations scale their security operations, the ability to ingest, process, and analyze high volumes of data reliably becomes increasingly critical. Microsoft Sentinel continues to expand its ecosystem through the Codeless Connector Framework (CCF), enabling ISVs to build and deliver integrations with Sentinel faster while simplifying deployment for customers. Today, CCF extends even further with support for Azure Blob Storage, introducing a new pattern for how data can be delivered into Sentinel. Expanding Connector Patterns with Azure Blob Storage CCF has traditionally enabled connectors that integrate directly with partner APIs and data sources. With this latest enhancement, ISVs can now build connectors that read data from Azure Blob Storage—unlocking new flexibility in how security data is collected and delivered. In this model, an ISV writes data to an Azure Blob Storage account. The Sentinel connector then reads from that storage layer, using Azure-native components such as Event Grid and storage queues to process events and forward them through data collection rules (DCR) into Log Analytics workspace. This approach introduces a durable data layer between the data source and Sentinel, enabling more resilient and scalable ingestion scenarios. Why a durable data layer matters By leveraging Azure Blob Storage as part of the ingestion pipeline, CCF connectors gain important operational advantages. This architecture allows data to be buffered and processed asynchronously, helping manage fluctuations in data volume and ensuring consistent delivery. Key benefits include: Resilience: Buffers spikes and handles backpressure to maintain steady ingestion Improved Compatibility: Supports widely adopted Azure Blob-based log streaming, enabling seamless integration with partners that already use Azure for audit data delivery Data protection: Reduces risk of data loss during outages or throttling Scalability: Supports high-volume ingestion scenarios across tenants Flexibility: Enables architectures that can support multiple SIEMs or data consumers Together, these capabilities make CCF Azure Blob Storage based connectors a strong fit for partners managing large, variable, or distributed data pipelines. Partner adoption Early partners are already taking advantage of this capability to modernize their integrations and support evolving customer needs. Cloudflare Cloudflare integrates with Microsoft Sentinel using the Codeless Connector Framework (CCF) to bring Cloudflare log data into centralized security operations workflows. The connector ingests Cloudflare logs—delivered via Logpush to Azure Blob Storage—into Sentinel for analysis, enabling security teams to correlate web, network, and application activity with other security signals. By combining Cloudflare’s global threat visibility with Sentinel analytics and automation, this integration supports more effective threat detection, investigation, and incident response across Cloudflare‑protected environments. Netskope Web Transaction Events Netskope integrates with Microsoft Sentinel to provide detailed visibility into web and cloud activity across users, applications, and SaaS services. The connector ingests Netskope web transaction logs into Sentinel—leveraging Azure Blob Storage as a staging layer for log streaming and ingestion—to enable near real‑time analysis of user behavior, policy violations, and potential threats. By combining Netskope’s inline web inspection with Sentinel’s analytics and correlation capabilities, this integration helps security teams detect risky activity, investigate incidents, and strengthen monitoring across modern cloud environments. These integrations demonstrate how Azure Blob Storage support can simplify ingestion architectures while improving reliability and scalability for customers. Here is what our partners say about the functionality. Cloudflare: Netskope: Get started Developers can begin building CCF Azure Blob Storage -enabled connectors today using the guidance on Microsoft Learn. This documentation provides step-by-step instructions for configuring storage, processing events, and connecting data to Sentinel. In the unlikely event that you encounter any issues in building or updating your connector, App Assure is here to help. We are an engineering-backed team committed to supporting customers and software development companies throughout their journey with Sentinel to streamline integration and accelerate time to market. Reach out to us via our intake form for assistance.
