Microsoft won’t send you SMS texts for login anymore – why it’s pushing passkeys instead


Authenticate a Microsoft account with a passkey

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ZDNET’s key takeaways

  • Microsoft is phasing out SMS as an authentication method.
  • SMS messages are unencrypted and vulnerable to hackers.
  • Microsoft account owners will be prompted to set up a passkey instead.

When trying to sign-in to or recover one of your online accounts, you’ll often receive a text message that prompts you to verify that you’re the account owner. But that SMS-based message is not a secure authentication method. Now, Microsoft is putting the brakes on it for anyone who uses a Microsoft account.

Also: How passkeys work: The complete guide to your inevitable passwordless future

On a new support page, Microsoft announced that it will start phasing out SMS as an authentication and account recovery method for personal Microsoft accounts. Instead, the company is pushing passkeys, which offer much stronger security.

What makes SMS authentication so insecure?

Why is SMS such a poor form of authentication? No matter which messaging app you use, SMS lacks end-to-end encryption to protect the text during its journey. As such, the message can be intercepted by hackers who then gain access to the included security code.

One common tactic is SIM swapping. Here, a hacker who snags your text can use the security code to sign in to your mobile account, thereby convincing your carrier to transfer your number to a different SIM. From there, they can receive SMS authentication texts sent to your number, allowing them to take over your personal accounts one by one.

“SMS-based authentication is now a leading source of fraud, and by moving to passwordless accounts, passkeys, and verified email, we’re helping you stay ahead of evolving threats while making account access simpler and more seamless,” Microsoft said on its support page. “SMS authentication is vulnerable to phishing and SIM-swap attacks. We’re replacing it with passkeys and verified email for better protection and convenience.”

Also: Should you stop logging in through Google and Facebook? Consider these SSO risks vs. benefits

Mobile carriers are certainly aware of the risks of SIM swapping, and many now offer SIM protection  that locks your phone line to guard against unauthorized changes. However, SMS is still an inherently weak and vulnerable authentication method.

With SMS on its way out, how would you verify a login or recovery for your Microsoft account? For that, Microsoft said it will guide you through the process of adding a verified email and passkey. If you’d rather not wait and want to set up a passkey right away, another Microsoft support page explains how to do that.

Yet another reason to use a password manager

One hiccup with passkeys is that they’re device-specific. What happens if you create a passkey on your computer but then need to use it on your mobile phone, or vice versa? To get past that barrier, Microsoft suggests using a password manager to store the passkey and use it on any device where the program is installed.

Most major password managers now support passkeys, including the Microsoft Password Manager in Edge, Google Password Manager, Apple Passwords, 1Password, NordPass, Bitwarden, and Dashlane.

Also: The best password managers: Expert tested

Another method is to save a passkey to a physical security key, which you can then plug into your PC or mobile device to authenticate your account. Alternatively, you can save the passkey on your mobile phone and scan it when you need to sign in on your computer. On Windows PCs, Windows Hello also supports passkeys. Whichever method you choose, you would typically use your face, fingerprint, security key, or PIN to sign in with the passkey.

Though the transition to passkeys does require several steps, the short-term pain is worth the long-term gain, as they say. I applaud Microsoft for making this change. I wish more companies would follow suit.





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ZDNET’s key takeaways

  • Trusted quality data is the backbone of agentic AI.
  • Identifying high-impact workflows to assign to AI agents is key to scaling adoption.
  • Scaling agentic AI starts with rethinking how work gets done. 

Gartner forecasts that worldwide AI spending will total $2.5 trillion in 2026, a 44% year-over-year increase. Spending on AI platforms for data science and machine learning will reach $31 billion, and spending on AI data will reach $3 billion.

The global agentic AI market will reach $8.5 billion by the end of 2026 and nearly $40 billion by 2030, per Deloitte Digital. Organizations are rapidly accelerating their adoption of AI agents, with the current average utilization standing at 12 agents per organization, according to MuleSoft 2026 research. This rate is projected to increase by 67% over the next two years, reaching an average of 20 AI agents. 

Also: How to build better AI agents for your business – without creating trust issues

According to IDC, by 2026, 40% of all Global 2000 job roles will involve working with AI agents, redefining long-held traditional entry, mid, and senior level positions. But the journey will not be smooth. By 2027, companies that do not prioritize high-quality, AI-ready data will struggle to scale generative AI and agentic solutions, resulting in a 15% loss in productivity. While 2025 was the year of pilot experiments and small production deployments of agentic AI, 2026 is shaping up to be the year of scaling agentic AI. And to scale agentic AI, according to IDC’s forecast, companies will need trustworthy, accessible, and quality data. 

Scaling agentic AI adoption in business requires a strong data foundation, according to McKinsey research. Businesses can create high-impact workflows by using agents, but to do so, they must modernize their data architecture, improve data quality, and advance their operating models. 

McKinsey found that nearly two-thirds of enterprises worldwide have experimented with agents, but fewer than 10% have scaled them to deliver measurable value. The biggest obstacle to scaling agent adoption is poor data — eight in ten companies cite data limitations as a roadblock to scaling agentic AI. 

Also: AI agents are fast, loose, and out of control, MIT study finds

McKinsey identified the top data limitations as primary constraints that companies face when scaling AI, including: operating model and talent constraints, data limitations, ineffective change management, and tech platform limitations. 

Data is the backbone of agentic AI

Research shows that agentic AI needs a steady flow of high-quality, trusted data to accurately automate complex business workflows. Successful agentic AI also depends on a data architecture that can support autonomy — executing tasks without human intervention. 

Two agentic usage models are emerging: single-agent workflows (one agent using multiple tools) and multi-agent workflows (specialized agents collaborate). In each case, agents will rely on access to high-quality data. Data silos and fragmented data would lead to errors and poor agentic decision-making. 

Four steps for preparing your data 

McKinsey identified four coordinated steps that connect strategy, technology, and people in order to build strong foundational data capabilities. 

Also: Prolonged AI use can be hazardous to your health and work: 4 ways to stay safe

  1. Identify high-impact workflows to ‘agentify’. Focus on highly deterministic, repetitive tasks that deliver value as strong candidates for AI agents. 

  2. Modernize each layer of the data architecture for agents. The focus on modernization should support interoperability, easy access, and governance across systems. The vast majority of business applications do not share data across platforms. According to MuleSoft research, organizations are rapidly adopting autonomous systems. The average enterprise now manages 957 applications — rising to 1,057 for those furthest along in their agentic AI journey. Only 27% of these applications are currently connected, creating a significant challenge for IT leaders aiming to meet their near-term AI implementation goals. 

  3. Ensure that data quality is in place. Businesses must ensure that both structured and unstructured data, as well as agent-generated data, meet consistent standards for accuracy, lineage, and governance. Access to trusted data is a key obstacle. IT teams now spend an average of 36% of their time designing, building, and testing new custom integrations between systems and data. Custom work will not help scale AI adoption. The most significant obstacle to successful AI or AI agent deployment is data quality, cited as the top concern by 25% of organizations. Furthermore, almost all organizations (96%) struggle to use data from across the business for AI initiatives.  

  4. Build an operating and governance model for agentic AI. This is about rethinking how work gets done. Human roles will shift from execution to supervision and orchestration of agent-led workflows. In a hybrid work environment, governance will dictate how agents can operate autonomously in a trustworthy, transparent, and scaled manner. 

The work assigned to AI agents 

McKinsey highlighted the importance of identifying a few critical workflows that would be candidates for AI agents to own. To begin, an end-to-end workflow mapping would help identify opportunities for agentic use. McKinsey found that AI adoption is led by customer service, marketing, knowledge management, and IT. It is important to identify clear metrics that validate impact. Teams should identify the data that can be reused across tasks and workflows.

Also: These companies are actually upskilling their workers for AI – here’s how they do it

McKinsey concludes that having access to high-quality data is a strategic differentiator in the agentic AI era. Because agents will generate enormous amounts of data, data quality, lineage, and standardization will be even more important in the agentic enterprise. And as agentic systems scale, governance becomes the primary level for control. The data foundation will be the competitive advantage in the agentic era. 





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