In February 2025, one of the largest breaches in crypto history stunned the digital asset world. Bybit, a major exchange, lost $1.5 billion in a targeted cyberattack. Despite using multi-factor authentication (MFA), password policies, and access controls, the attackers succeeded—swiftly and silently.
This breach didn’t happen due to negligence. It happened because of a fundamental flaw in the industry’s security model.
The attack on Bybit revealed what many have suspected: identity-based security no longer works. And unless the industry evolves, these breaches will keep happening.
The Fatal Flaw: Identity Is Not Access
For years, digital asset platforms have relied on identity-based models—verify who someone is, then let them in. These systems use usernames, passwords, and tokens to confirm identity.
But here’s the problem: identification doesn’t prove authorization. Verifying who someone is doesn’t mean they should access a system. This identity-authentication gap is a critical weakness—and attackers exploit it daily.
In identity-based setups, one stolen credential—be it a password or API key—can open up entire systems. And once inside, attackers move laterally across platforms, wallets, and services. It’s exactly what happened at Bybit.
AI Has Outpaced Traditional Security
The rise of AI-powered attacks has made identity-based security even more vulnerable. Attackers now use automation, deepfakes, and hyper-targeted phishing to bypass human safeguards.
A 2024 Capgemini report showed that 97% of organizations experienced breaches linked to AI-generated threats. In Bybit’s case, attackers used phishing, SIM-swapping, and intercepted MFA codes to break through. Once they had access, the damage was instant—and irreversible.
Legacy tools like MFA and password managers can’t keep up. The attack surface has changed, but the defense strategy hasn’t.
A New Model: Encryption-Based Access with ML-DAES
To prevent these failures, the industry needs a new foundation. That’s where ML-DAES (Multi-Layer Dynamic Access Encryption Security) comes in.
ML-DAES eliminates passwords and API keys. Instead, it uses encrypted, application-specific credentials that users never see, store, or share. There’s nothing to phish, steal, or misuse. And because every credential is system-specific, even if one access point is compromised, it can’t be used elsewhere.
This shifts authentication away from identity toward encrypted authorization—a model that neutralizes phishing, prevents lateral movement, and removes insider credential risk.
What If Bybit Had Used ML-DAES?
Phishing emails would fail—there’d be no credentials to hand over.
SIM-swapping and MFA interception would be irrelevant.
API keys wouldn’t exist in a usable format.
Even if attackers breached one system, they’d go no further. ML-DAES would have segmented access, blocked lateral movement, and rendered stolen access useless.
In short: the breach wouldn’t have happened.
Beyond Security: Compliance and Efficiency
ML-DAES also automates compliance. With immutable, cryptographically signed access logs, firms meet regulations like MiCA, SEC, and GDPR effortlessly.
IT teams save time with no password resets or manual provisioning. And with fewer breaches, insurers lower premiums. The result is stronger security, smoother operations, and reduced costs.
The Industry Must Move Forward
The Bybit attack isn’t just a cautionary tale—it’s a turning point. Identity-based security is no longer viable in a world of AI threats and automated exploits.
To protect billions in digital assets—and the trust of investors—the industry must transition to encryption-based access models. ML-DAES offers a proven way forward.
The time to act is now. Not with more patches, but with a complete security rethink—one built for today’s threats, not yesterday’s assumptions.
