Discord adds end-to-end encryption to voice and video calls by default


Discord adds end-to-end encryption to voice and video calls by default

Pierluigi Paganini
May 21, 2026

Discord now enables end-to-end encryption by default for all voice and video calls, making conversations inaccessible even to the platform itself.

No announcement fanfare, no opt-in required, no settings to dig through. Discord flipped a switch on Monday and end-to-end encryption is now the default for every voice and video call on the platform. If you used Discord to call someone today, that conversation was encrypted in a way that even Discord cannot access.

“End-to-end Encryption is now standard for every voice and video call on Discord, outside of stage channels. No opt-in required.” announced Discord.

That is a bigger deal than it might sound, especially right now.

The timing is notable. Earlier this month, Meta quietly removed end-to-end encryption from Instagram’s direct messaging feature, a step backward that drew criticism but not much sustained attention. TikTok also confirmed it would not be adding end-to-end encryption to direct messages. Two of the largest social platforms in the world are moving away from private communications, while Discord moves toward it. The contrast is hard to miss.

Discord has been building toward this for a while. The company launched end-to-end encrypted voice and video calling back in 2024, initially as an opt-in feature.

“It’s been quite a journey since then. In September 2024, Stephen Birarda introduced the DAVE protocol: an open, audited end-to-end encryption protocol for audio and video. We began migrating calls on desktop and mobile and started proving that E2EE could operate at Discord’s scale without compromising the experience people expect from us.” reads the announcement. “In 2025, Clément Brisset extended DAVE to every remaining platform, including web browsers, gaming consoles, support for Discord bots/apps, and our Social SDK, helping close the gaps that had kept some calls from being fully encrypted. And at the beginning of March 2026, we completed that migration. “

Monday’s change simply made it the default for everyone, automatically, with no action needed on the user’s side. Stage channels are the only exception, those are designed for broadcast-style communication where the expectation of privacy is different.

Discord said its DAVE encryption protocol was designed to support voice and video calls across diverse devices like PCs, phones, consoles, and browsers with minimal latency. The protocol and implementation are open-source, externally audited by Trail of Bits, and covered by a bug bounty program. Discord also worked with Mozilla to fix a Firefox issue affecting encrypted calls, aiming for a seamless transition for users.

“As of early March 2026, every voice and video call on Discord, whether in DMs, group DMs, voice channels, or Go Live streams, is end-to-end encrypted by default. To complete that migration, we required all clients to support DAVE before joining a call.” continues the announcement. “We are now in the process of removing the client code that supports unencrypted fallback. After that is done, it will not be possible to fall back to unencrypted connections.”

For a platform with hundreds of millions of users, many of them younger people using Discord as their primary way to hang out with friends online, this is a meaningful baseline privacy upgrade that most of them will never have to think about. It just works, in the background, on every call.

The broader context here is worth sitting with for a moment. End-to-end encryption for messaging and calling has been a live debate for years, caught between genuine privacy advocates, law enforcement agencies that argue it hampers investigations, and platform companies navigating both. Discord has landed clearly on one side of that debate, at least for voice and video, and has done it in the most user-friendly way possible: by making it the default rather than something you have to seek out in a settings menu.

It is unclear whether Discord extends the same protection to text messages. For now, the voice and video change alone puts it ahead of most mainstream social platforms on this specific privacy dimension, at a moment when several of those platforms are going in the opposite direction.

Follow me on Twitter: @securityaffairs and Facebook and Mastodon

Pierluigi Paganini

(SecurityAffairs – hacking, end-to-end encryption)







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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. 

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  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.

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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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