MyCena® Introduces ML-DAES: A New Security Standard for the AI Era
The cybersecurity game has changed—and not in our favor.
AI has supercharged cybercrime, giving attackers industrial-scale tools to automate phishing, forge identities, hijack APIs, and exploit security gaps at lightning speed. From deepfakes convincing employees to transfer funds to synthetic identities sliding through KYC systems, the scale and sophistication of threats are escalating rapidly.
But the real crisis lies deeper: the foundation of modern cybersecurity is broken. Identity-based models—like IAM, MFA, SSO, and PAM—rely on a flawed assumption that verifying who you are is the same as confirming whether you should have access. This confusion creates a single point of failure—credentials that attackers can steal, forge, or manipulate.
It’s time for a new standard.
And that’s what MyCena is delivering with ML-DAES: Multi-Layer Dynamic Access Encryption Security.
The Problem with Identity-Based Security
For decades, cybersecurity has leaned on identity: usernames, passwords, biometrics, tokens. These are used to verify someone’s identity and then grant access. But AI doesn’t care who you are—it cares about the systems you can reach once you’re inside.
This model has a critical flaw called the identification-authentication gap. Simply put, identity systems verify who you are, but not whether you should be accessing a particular resource. Once a credential is compromised—whether stolen through phishing, guessed via brute force, or leaked from a third-party system—it opens a direct path into your network.
The 2023 MOVEit breach, SolarWinds, and countless other incidents share a common thread: one credential led to many compromised systems. In fact, 86% of breaches involve stolen credentials, and 78% of employees reuse passwords across systems. With AI now driving spear phishing, credential stuffing, and synthetic identity attacks, this model is no longer sustainable.
Introducing ML-DAES: No Human-Managed Credentials
ML-DAES offers a paradigm shift. Instead of relying on identity and passwords, it introduces encryption-based authentication powered by dynamically generated, application-specific credentials that are never seen, stored, or managed by humans.
Here’s how it works:
When a user needs access to a system, ML-DAES generates a one-time encrypted credential specifically for that system. That credential is valid only for that resource and disappears after use. The user doesn’t know it, store it, or share it. There’s nothing to phish, nothing to steal, nothing to misuse.
Even if attackers gain entry to one system, they can’t move laterally—each system requires a unique encrypted credential that can’t be reused. And because there’s no human involvement, insider threats and social engineering tactics become irrelevant.
This isn’t just security—it’s segmented AI-resilient access.
The Math Behind the Model
At the core of ML-DAES is mathematical combinatorics, which powers its dynamic encryption structure. Credentials are created through multiple randomized layers of encryption. Even if one layer is breached, the rest remain intact—making brute-force or AI-generated replication virtually impossible.
Unlike biometrics or password hashes, which can be reverse-engineered or cloned, ML-DAES credentials are entirely machine-generated and specific each the system. In essence, ML-DAES doesn’t just strengthen authentication—it makes the very concept of credential theft obsolete.
Real-World Impact: More Than Security
ML-DAES doesn’t only stop attacks—it transforms how organizations operate:
AI has supercharged cybercrime, giving attackers industrial-scale tools to automate phishing, forge identities, hijack APIs, and exploit security gaps at lightning speed. From deepfakes convincing employees to transfer funds to synthetic identities sliding through KYC systems, the scale and sophistication of threats are escalating rapidly.
But the real crisis lies deeper: the foundation of modern cybersecurity is broken. Identity-based models—like IAM, MFA, SSO, and PAM—rely on a flawed assumption that verifying who you are is the same as confirming whether you should have access. This confusion creates a single point of failure—credentials that attackers can steal, forge, or manipulate.
It’s time for a new standard.
And that’s what MyCena is delivering with ML-DAES: Multi-Layer Dynamic Access Encryption Security.
The Problem with Identity-Based Security
For decades, cybersecurity has leaned on identity: usernames, passwords, biometrics, tokens. These are used to verify someone’s identity and then grant access. But AI doesn’t care who you are—it cares about the systems you can reach once you’re inside.
This model has a critical flaw called the identification-authentication gap. Simply put, identity systems verify who you are, but not whether you should be accessing a particular resource. Once a credential is compromised—whether stolen through phishing, guessed via brute force, or leaked from a third-party system—it opens a direct path into your network.
The 2023 MOVEit breach, SolarWinds, and countless other incidents share a common thread: one credential led to many compromised systems. In fact, 86% of breaches involve stolen credentials, and 78% of employees reuse passwords across systems. With AI now driving spear phishing, credential stuffing, and synthetic identity attacks, this model is no longer sustainable.
Introducing ML-DAES: No Human-Managed Credentials
ML-DAES offers a paradigm shift. Instead of relying on identity and passwords, it introduces encryption-based authentication powered by dynamically generated, application-specific credentials that are never seen, stored, or managed by humans.
Here’s how it works:
When a user needs access to a system, ML-DAES generates a one-time encrypted credential specifically for that system. That credential is valid only for that resource and disappears after use. The user doesn’t know it, store it, or share it. There’s nothing to phish, nothing to steal, nothing to misuse.
Even if attackers gain entry to one system, they can’t move laterally—each system requires a unique encrypted credential that can’t be reused. And because there’s no human involvement, insider threats and social engineering tactics become irrelevant.
This isn’t just security—it’s segmented AI-resilient access.
The Math Behind the Model
At the core of ML-DAES is mathematical combinatorics, which powers its dynamic encryption structure. Credentials are created through multiple randomized layers of encryption. Even if one layer is breached, the rest remain intact—making brute-force or AI-generated replication virtually impossible.
Unlike biometrics or password hashes, which can be reverse-engineered or cloned, ML-DAES credentials are entirely machine-generated and specific each the system. In essence, ML-DAES doesn’t just strengthen authentication—it makes the very concept of credential theft obsolete.
Real-World Impact: More Than Security
ML-DAES doesn’t only stop attacks—it transforms how organizations operate:
- Phishing attacks fail because there’s no credential to steal.
- Compliance becomes easier with automated access logs that align with GDPR, DORA, and ISO standards.
- IT burden is reduced—no more password resets, no MFA fatigue, no access provisioning chaos.
- Audit readiness is built-in, with cryptographically signed access trails.
- It also integrates easily into existing IAM, PAM, and SSO environments, offering a clean, non-disruptive evolution away from credential-based architecture.
Why Now? A Security Standard for the AI Era
AI has made yesterday’s cybersecurity tools obsolete. Every delay in moving away from identity-based security increases risk, raises insurance premiums, and exposes organizations to growing threats. In the past, security upgrades were optional. Today, they’re existential.
ML-DAES represents not just a tool—but a new security standard. One that removes human error, resists AI automation, simplifies compliance, and restores trust in access control.
For CISOs, regulators, and IT leaders, this is more than an upgrade. It’s an opportunity to eliminate the #1 cause of breaches—human-managed credentials—once and for all.
Take the Next Step
You can’t stop AI-powered threats from targeting your systems.
But with ML-DAES, you can stop them from getting in.
Explore how encryption-based access can replace identity-based vulnerabilities in your organization.
To learn how ML-DAES is setting the new global standard in secure access for the AI age.