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.Entra ID - Federation or EAM for Passkey deployment
Question for the community: Looking to replace Entra to ADFS federation and also implement passkey. Have an existing deployment with a third party MFA provider. I want to limit the disruption for my end users as much as possible and am looking at 2 possible paths forward: Replace ADFS with third party, allowing Passkey and push from the third party, as well as first factor auth. Move to Entra staged rollout with EAM and have Entra deal with first factor. I know this wont include the ability to use third party first factor. Issues I am seeing is that the system browser setting needs to be enabled on the tenant and cant enforce external auth strengths in entra conditional access. All roads keep leading back to Entra which is fine with me but I do not currently have a P2 license for the advanced risk analytics. My third party currently has that capability. Would also love to know any rumblings of authentication strenght support for External MFA. What are some of the configurations out there that you all are doing and any pros/cons of each.
Passkey/FIDO2 for users in mainland China – recommended approach?
Hi, We are currently evaluating the adoption of Passkey/FIDO2 in Microsoft Entra ID as part of our strategy to move users away from traditional authentication methods such as SMS and voice and towards phishing-resistant authentication. Our Microsoft Entra ID tenant is a global tenant (not Azure China operated by 21Vianet), but we have a group of users who are physically located in mainland China. For users in other regions, we are evaluating Passkeys in Microsoft Authenticator and FIDO2 security keys. However, before defining the approach for our users in China, we would like to better understand the supported and recommended architecture for this particular scenario. We have reviewed the Microsoft documentation and understand that: Passkeys are not available for Microsoft Azure operated by 21Vianet. On iOS, Microsoft Authenticator Passkeys can be used to sign in to global organizations while the user is in China. On Android, Passkey authentication has dependencies such as Google Play Services, which raises questions for devices operating in mainland China. Our main questions are: For users physically located in mainland China but authenticating against a global Microsoft Entra ID tenant, what is Microsoft's recommended approach for deploying phishing-resistant authentication with Passkey/FIDO2? Is Microsoft Authenticator Passkey on iOS considered a fully supported option for users permanently located in mainland China, rather than only users temporarily traveling there? For Android users in mainland China, what is the recommended approach considering the dependency on Google Play Services? Is Passkey in Microsoft Authenticator a realistic supported option in this scenario? Would physical FIDO2 security keys be the preferred or more consistent alternative for users in China when mobile Passkeys cannot be reliably used? Are there any other Microsoft-supported Passkey providers or authentication approaches that should be considered specifically for mainland China? From an enrollment perspective, what would be the recommended bootstrap process for these users? For example, would Temporary Access Pass (TAP) be the recommended method to allow users to register their Passkey/FIDO2 credential before SMS/Voice is removed? Are there any additional regional, connectivity, mobile OS, or application compatibility considerations that should be taken into account before enforcing a phishing-resistant Authentication Strength through Conditional Access? Our objective is not simply to enable Passkey in the tenant, but to define a supported and sustainable authentication model for users located in mainland China before starting a broader rollout. Any experience, Microsoft guidance, or recommended architecture for this type of scenario would be greatly appreciated. Thanks in advance.
Entra ID 53003: How to identify primary authentication when Authentication Details are empty?
We are investigating repeated interactive Microsoft Entra ID sign-ins with the following pattern: - ErrorCode: 53003 (BlockedByConditionalAccess) - isInteractive: true - AppDisplayName: OfficeHome - Authentication requirement: Multifactor authentication Conditional Access correctly blocks these requests because they originate outside our trusted locations. We have already confirmed through controlled testing that the authentication flow can reach 53003 after the correct credentials are provided. Our current problem is different: We need to determine what mechanism satisfied the primary/first authentication factor for the observed 53003 events. For these events, Authentication Details are empty: - Authentication method: empty - Authentication method detail: empty - Result detail: empty Therefore, we cannot determine whether primary authentication was performed using a password, an existing token/session claim, PHS, PTA, federation, Seamless SSO, or another mechanism. Our questions are: 1. Is there any tenant-visible telemetry that can identify the primary authentication mechanism when AuthenticationDetails is empty? 2. Can fields such as AuthenticationProcessingDetails, AuthenticationProtocol, IncomingTokenType, OriginalTransferMethod, or AuthenticationMethodsUsed reliably distinguish between password validation and reuse of an existing authentication/token? 3. If additional authentication infrastructure is used: - can PTA Authentication Agent logs be correlated using RequestId or CorrelationId? - should Seamless SSO generate a corresponding Kerberos 4769 event for AZUREADSSOACC$? - should federated authentication be traceable in AD FS logs? 4. For Password Hash Synchronization, is there any tenant-visible telemetry confirming that Entra ID actually validated the password for a specific sign-in request? 5. If this information is not exposed to the tenant, can Microsoft Support retrieve backend authentication telemetry for a specific Sign-in ID / RequestId / CorrelationId and determine how primary authentication was satisfied? Our goal is not to determine why Conditional Access generated 53003 — that part is already understood. We specifically need to identify what satisfied primary authentication before the Conditional Access evaluation.
Free, offline tool: see each user's real passkey policy in Entra ID and who SMS retirement blocks
Entra combines passkey profiles as a union (any profile that allows a passkey type wins), while Conditional Access works as an intersection. So a Global Admin in a strict "security keys only" profile who is also in an All Users profile can still register a synced iCloud or Google passkey, and the portal never shows you that per user. I built EffectivePasskey to answer three questions for every user and admin: 1. What passkey can this person actually register? 2. What can this admin actually sign in with after Conditional Access? 3. Who gets blocked when SMS and voice MFA retire (1 Feb 2027, and 1 Jul 2027 for Global Admins)? It runs entirely on your machine from read-only JSON exports. It never signs in to your tenant and never changes anything. No telemetry. MIT licensed and completely free. Try it with the included demo tenants: pip install effective-passkey GitHub: https://github.com/xsignon-secure/EffectivePasskey Feedback and issues are very welcome, especially from anyone mid-rollout.Fido passkeys blocked by policy
Hi all I'm helping out a customer with deploying physical passkeys and I'm running into a weird error. I've activated the sign in method and selected the two AAGuids for the Authenticator app and I've added the right AAGuid for the brand and model of passkey we are using. We can select the authentication method and enroll the security correctly but when trying to sign in using it we get the error as displayed in the attached picture. When checking the sign in logs i get this error message FIDO sign-in is disabled via policy and the error code is: 135016 I've not been able to track down any policy that would be blocking passkeys. anyone got any ideas?
passkeys in the Authenticator app regarding attestation
I have a question about passkeys in the Authenticator app regarding attestation in connection with QR code-based cross-device sign-in. When we register a passkey with attestation enabled in the Authenticator app, it can be used to complete the sign-in process on another device via QR code and Bluetooth Low Energy. According to Microsoft’s documentation, this shouldn’t be possible with attestation enabled, yet it works. What are we misunderstanding here? https://learn.microsoft.com/en-us/entra/identity/authentication/how-to-enable-authenticator-passkey Thanks for your inputs. JohannesWhy UK Enterprise Cybersecurity Is Failing in 2026 (And What Leaders Must Change)
Enterprise cybersecurity in large organisations has always been an asymmetric game. But with the rise of AI‑enabled cyber attacks, that imbalance has widened dramatically - particularly for UK and EMEA enterprises operating complex cloud, SaaS, and identity‑driven environments. Microsoft Threat Intelligence and Microsoft Defender Security Research have publicly reported a clear shift in how attackers operate: AI is now embedded across the entire attack lifecycle. Threat actors use AI to accelerate reconnaissance, generate highly targeted phishing at scale, automate infrastructure, and adapt tactics in real time - dramatically reducing the time required to move from initial access to business impact. In recent months, Microsoft has documented AI‑enabled phishing campaigns abusing legitimate authentication mechanisms, including OAuth and device‑code flows, to compromise enterprise accounts at scale. These attacks rely on automation, dynamic code generation, and highly personalised lures - not on exploiting traditional vulnerabilities or stealing passwords. The Reality Gap: Adaptive Attackers vs. Static Enterprise Defences Meanwhile, many UK enterprises still rely on legacy cybersecurity controls designed for a very different threat model - one rooted in a far more predictable world. This creates a dangerous "Resilience Gap." Here is why your current stack is failing- and the C-Suite strategy required to fix it. 1. The Failure of Traditional Antivirus in the AI Era Traditional antivirus (AV) relies on static signatures and hashes. It assumes malicious code remains identical across different targets. AI has rendered this assumption obsolete. Modern malware now uses automated mutation to generate unique code variants at execution time, and adapts behaviour based on its environment. Microsoft Threat Intelligence has observed threat actors using AI‑assisted tooling to rapidly rewrite payload components, ensuring that every deployment looks subtly different. In this model, there is no reliable signature to detect. By the time a pattern exists, the attacker has already moved on. Signature‑based detection is not just slow - it is structurally misaligned with AI‑driven attacks. The Risk: If your security relies on "recognising" a threat, you are already breached. By the time a signature exists, the attacker has evolved. The C-Suite Pivot: Shift investment from artifact detection to EDR/XDR (Extended Detection and Response). We must prioritise behavioural analytics and machine learning models that identify intent rather than file names. 2. Why Perimeter Firewalls Fail in a Cloud-First World Many UK enterprise still rely on firewalls enforcing static allow/deny rules based on IP addresses and ports. This model worked when applications were predictable and networks clearly segmented. Today, enterprise traffic is encrypted, cloud‑hosted, API‑driven, and deeply integrated with SaaS and identity services. AI‑assisted phishing campaigns abusing OAuth and device‑code flows demonstrate this clearly. From a network perspective, everything looks legitimate: HTTPS traffic to trusted identity providers. No suspicious port. No malicious domain. Yet the attacker successfully compromises identity. The Risk: Traditional firewalls are "blind" to identity-based breaches in cloud environments. The C-Suite Pivot: Move to Identity-First Security. Treat Identity as the new Control Plane, integrating signals like user risk, device health, and geolocation into every access decision. 3. The Critical Weakness of Single-Factor Authentication Despite clear NCSC guidance, single-factor passwords remain a common vulnerability in legacy applications and VPNs. AI-driven credential abuse has changed the economics of these attacks. Threat actors now deploy adaptive phishing campaigns that evolve in real-time. Microsoft has observed attackers using AI to hyper-target high-value UK identities- specifically CEOs, Finance Directors, and Procurement leads. The Risk: Static passwords are now the primary weak link in UK supply chain security. The C-Suite Pivot: Mandate Phishing‑resistant MFA (Passkeys or hardware security keys). Implement Conditional Access policies that evaluate risk dynamically at the moment of access, not just at login. Legacy Security vs. AI‑Era Reality 4. The Inherent Risk of VPN-Centric Security VPNs were built on a flawed assumption: that anyone "inside" the network is trustworthy. In 2026, this logic is a liability. AI-assisted attackers now use automation to map internal networks and identify escalation paths the moment they gain VPN access. Furthermore, Microsoft has tracked nation-state actors using AI to create synthetic employee identities- complete with fake resumes and deepfake communication. In these scenarios, VPN access isn't "hacked"; it is legally granted to a fraudster. The Risk: A compromised VPN gives an attacker the "keys to the kingdom." The C-Suite Pivot: Transition to Zero Trust Architecture (ZTA). Access must be explicit, scoped to the specific application, and continuously re‑evaluated using behavioural signals. 5. Data: The High-Velocity Target Sensitive data sitting unencrypted in legacy databases or backups is a ticking time bomb. In the AI era, data discovery is no longer a slow, manual process for a hacker. Attackers now use AI to instantly analyse your directory structures, classify your files, and prioritise high-value data for theft. Unencrypted data significantly increases your "blast radius," turning a containable incident into a catastrophic board-level crisis. The Risk: Beyond the technical breach, unencrypted data leads to massive UK GDPR fines and irreparable brand damage. The C-Suite Pivot: Adopt Data-Centric Security. Implement encryption by default, classify data while adding sensitivity labels and start board-level discussions regarding post‑quantum cryptography (PQC) to future-proof your most sensitive assets. 6. The Failure of Static IDS Traditional Intrusion Detection Systems (IDS) rely on known indicators of compromise - assuming attackers reuse the same tools and techniques. AI‑driven attacks deliberately avoid that assumption. Threat actors are now using Large Language Models (LLMs) to weaponize newly disclosed vulnerabilities within hours. While your team waits for a "known pattern" to be updated in your system, the attacker is already using a custom, AI-generated exploit. The Risk: Your team is defending against yesterday's news while the attacker is moving at machine speed. The C-Suite Pivot: Invest in Adaptive Threat Detection. Move toward Graph‑based XDR platforms that correlate signals across email, endpoint, and cloud to automate investigation and response before the damage spreads. From Static Security to Continuous Security Closing Thought: Security Is a Journey, Not a Destination For UK enterprises, the shift toward adaptive cybersecurity is no longer optional - it is increasingly driven by regulatory expectation, board oversight, and accountability for operational resilience. Recent UK cyber resilience reforms and evolving regulatory frameworks signal a clear direction of travel: cybersecurity is now a board‑level responsibility, not a back‑office technical concern. Directors and executive leaders are expected to demonstrate effective governance, risk ownership, and preparedness for cyber disruption - particularly as AI reshapes the threat landscape. AI is not a future cybersecurity problem. It is a current force multiplier for attackers, exposing the limits of legacy enterprise security architectures faster than many organisations are willing to admit. The uncomfortable truth for boards in 2026 is that no enterprise is 100% secure. Intrusions are inevitable. Credentials will be compromised. Controls will be tested. The difference between a resilient enterprise and a vulnerable one is not the absence of incidents, but how risk is managed when they occur. In mature organisations, this means assuming breach and designing for containment: Access controls that limit blast radius Least privilege and conditional access restricting attackers to the smallest possible scope if an identity is compromised Data‑centric security using automated classification and encryption, ensuring that even when access is misused, sensitive data cannot be freely exfiltrated As a Senior Enterprise Cybersecurity Architect, I see this moment as a unique opportunity. AI adoption does not have to repeat the mistakes of earlier technology waves, where innovation moved fast and security followed years later. We now have a rare chance to embed security from day one - designing identity controls, data boundaries, automated monitoring, and governance before AI systems become business‑critical. When security is built in upfront, enterprises don’t just reduce risk - they gain the confidence to move faster and unlock AI’s value safely. Security is no longer a “department”. In the age of AI, it is a continuous business function - essential to preserving trust and maintaining operational continuity as attackers move at machine speed. References: Inside an AI‑enabled device code phishing campaign | Microsoft Security Blog AI as tradecraft: How threat actors operationalize AI | Microsoft Security Blog Detecting and analyzing prompt abuse in AI tools | Microsoft Security Blog Post-Quantum Cryptography | CSRC Microsoft Digital Defense Report 2025 | Microsoft https://www.ncsc.gov.uk/news/government-adopt-passkey-technology-digital-services
Disabling PIN-based login on Entra-joined PCs
Hi guys. Yesterday I took two machines off the domain and Entra joined them. The goal was 1) remove their access to domain resources 2) have tenant users login to the machine and get enriched tokens every time. this works as desired. The problem is every user gets prompted to set a pin. these are both shared secondary/tertiary PC's - there is no point to having a 6 digit PIN on them. I thought the new Authentication Methods tools had controls for this, but apparently not. A script was run to change certain related Reg Keys (by my onsite tech) but this had no change on reboot. textreg add "HKEY_LOCAL_MACHINE\SOFTWARE\Policies\Microsoft\PassportForWork" /v Enabled /t REG_DWORD /d 0 /freg add "HKEY_LOCAL_MACHINE\SOFTWARE\Policies\Microsoft\PassportForWork" /v DisablePostLogonProvisioning /t REG_DWORD /d 1 /f HKEY_LOCAL_MACHINE\SOFTWARE\Policies\Microsoft\PassportForWork Enabled key was set to 0, and DisablePostLogonProvisioning was set to 1. These are from various help threads I found here and other resources. Unfortunately, they do not work. Not sure what to do here. I've read there are InTune controls for this - but I don't really have the time to work out WindowsPC ennrollment profiles for 2 machines. The site has InTune, but only for iOS mobile management. Thoughts?
