Passkeys, Not Passcodes: A Practical Enterprise Guide to Moving Beyond Passwords

There is a small terminology problem in the identity world right now, and it matters more than it looks.

passcode or PIN is usually a local unlock secret. It unlocks a phone, a laptop, Windows Hello, an authenticator app, or a hardware security key. A passkey is different. A passkey is the standards-based replacement for passwords, built on FIDO2/WebAuthn. The user unlocks the passkey locally with a fingerprint, face scan, device PIN, pattern, or security key, but the website or application receives cryptographic proof — not a reusable password. FIDO defines passkeys as FIDO authentication credentials based on FIDO standards, tied to an account, and used with the same process the user already uses to unlock a device. 

That distinction is not pedantry. It is the difference between a local unlock method and a replacement for one of the most abused controls in the history of computing.

Passwords have had a long run. They also have had a long list of failures: reuse, phishing, spraying, stuffing, database theft, weak reset workflows, help desk abuse, and user fatigue. We have spent decades trying to compensate for those failures with complexity rules, expiration schedules, password managers, SMS codes, mobile push prompts, training campaigns, and detective controls.

Some of those helped. Some just moved the pain around.

Passkeys change the model.

They are not merely “better passwords.” They are a different authentication architecture.

A hacker is seated in front of a computer fingers poised over the keyboard They are ready to break into a system and gain access to sensitive information 6466041

The Problem: Passwords Are Shared Secrets in a World Built to Steal Them

A password proves identity by revealing a secret. That is the root of the problem.

When users type passwords into websites, there is always a chance they will type them into the wrong website. When companies store password material, there is always a chance attackers will steal it. When people reuse passwords, a breach in one place becomes an entry point somewhere else. When attackers automate guessing, weak and reused passwords become an industrial-scale attack surface.

Microsoft’s 2025 Digital Defense Report says 97% of identity attacks were password spray attacks, which is a pretty direct reminder that attackers still love the boring stuff that works. Verizon’s 2026 DBIR highlights that breaches continue to involve the human element, phishing, stolen credentials, ransomware, and software vulnerability exploitation — and also reports that 31% of breaches now start with software vulnerabilities, beating stolen passwords as the top initial entry point in that dataset. 

That combination matters. It tells us two things at once.

First, passwords remain a major identity risk. Second, replacing passwords is not the whole security program.

That is the right mental model for passkeys: they are a major improvement in authentication, not a magic shield around the enterprise.

What a Passkey Does Differently

A password is something the user knows and types.

A passkey is a cryptographic credential. When the user registers a passkey for a site or application, the device creates a unique public/private key pair. The private key stays with the authenticator or passkey provider. The public key is registered with the service. At sign-in, the service sends a fresh challenge. The authenticator signs the challenge with the private key. The service verifies the response with the public key.

No reusable password crosses the wire.

No password database needs to be protected in the same way.

No user has to remember whether the login page looks slightly wrong.

The protocol carries a lot of the security burden that we previously dumped on the user.

That is the real breakthrough.

FIDO describes passkeys as password replacement technology that uses cryptographic key pairs for phishing-resistant sign-in. It also notes that passkeys can be synced across devices or bound to a particular device. Microsoft Entra describes the same basic model: the private key is stored on the user device, the public key is stored with the app or website, and both unique keys are needed to sign in. 

The user experience is simple: unlock the device.

The security model is not simple — and that is a good thing.

The Plain-English Explanation for Users

For users, do not start with asymmetric cryptography. Start with what changes for them.

“A passkey is a safer way to sign in without typing a password. Instead of remembering and entering a password, you unlock your phone, laptop, or security key. The website gets proof that your device has the right key, but it never gets a password. That means there is no password for you to forget, reuse, mistype, or accidentally give to a fake website.”

That is enough for most end users.

Then answer the question they are really asking:

Does the website get my fingerprint or face scan?

No. The biometric check happens locally. FIDO states that biometric information and processing remain on the device and are not sent to a remote server; the server receives assurance that the biometric check succeeded. 

Is my device PIN now my corporate password?

No. NIST distinguishes centrally verified passwords from local activation secrets. A device PIN or unlock secret used locally to access an authenticator is not sent to the verifier the way a website password is. 

That is an important communication point. Users often hear “PIN” and think “weak password.” In a passkey model, the PIN is usually a local unlock mechanism protecting the private key, not the secret being verified by the website.

Why Passkeys Reduce Risk

Passkeys reduce several common attack paths:

Risk How passkeys help
Phishing The user does not type a reusable password, and the passkey is scoped to the legitimate relying party. A fake site should not be able to obtain a valid assertion for the real site.
Credential stuffing There is no shared password to reuse from another breach.
Password spraying Attackers cannot guess a password that is no longer accepted for that workflow.
Password database theft The service stores public key material rather than reusable passwords.
Weak MFA interception Passkeys can replace password plus SMS OTP, password plus TOTP, or password plus push approval in many use cases.
User fatigue Users approve sign-in with a familiar local unlock gesture rather than remembering and typing complex passwords.

FIDO states that passkeys are resistant to phishing, designed without shared secrets, and can replace legacy MFA flows such as password plus SMS OTP. FIDO also notes that common second factors such as OTPs and phone approvals remain phishable. NIST is similarly direct: passwords are not phishing-resistant, and authenticator outputs manually entered into an impostor verifier — such as OTP-style flows — are not considered phishing-resistant because they can be relayed. 

That last point is key.

A lot of organizations believe they solved phishing because they deployed MFA. In many cases, they deployed phishable MFA. That is better than passwords alone, but it is not the same as phishing-resistant authentication.

What Actually Happens Under the Hood

There are two ceremonies that matter: registration and authentication.

Registration

When a user creates a passkey:

  1. The user starts registration through an approved enrollment path.
  2. The relying party sends registration options to the browser or application.
  3. The browser or app calls the WebAuthn API.
  4. The authenticator creates a new public/private key pair scoped to that relying party.
  5. The private key stays in the authenticator or passkey provider.
  6. The public key, credential ID, user handle, flags, and optional attestation data are returned.
  7. The relying party stores the credential record with the user account.

W3C WebAuthn describes a model where the public key is returned to the relying party during registration, while the private key is bound to the authenticator and is expected not to be exposed. It also describes the credential record that the relying party stores for later authentication ceremonies. 

Authentication

When the user signs in later:

  1. The relying party generates a fresh random challenge.
  2. The browser or app sends the challenge and relying-party information to the authenticator.
  3. The authenticator checks whether it has a credential scoped to that relying party.
  4. The user performs local verification, such as biometric, PIN, device unlock, or security-key touch.
  5. The authenticator signs the challenge and relevant context.
  6. The relying party verifies the signature using the stored public key.
  7. The relying party checks the challenge, origin, RP ID, user verification flags, and policy requirements before granting access.

WebAuthn depends on randomized challenges to prevent replay attacks, and the relying party must generate those challenges in a trusted environment and verify that the returned challenge matches. 

This is why passkeys are different from passwords. A password login proves identity by disclosing a shared secret. A passkey login proves possession of a private key without disclosing it.

Why Phishing Resistance Works

The important concept is origin binding or relying party binding.

A passkey created for one legitimate service is not supposed to work for an attacker’s lookalike domain. A fake site may fool the human eye, but it should not be able to get a valid passkey assertion for the real service’s relying party ID.

W3C WebAuthn notes that credentials are scoped to a specific relying party and that only that relying party, identified by its RP ID, can use the credential in authentication ceremonies. It also warns relying parties not to accept unexpected origins, because origin validation is an additional layer of protection. 

That is the practical security gain.

The protocol stops relying solely on user vigilance.

We should still train users. We should still harden browsers. We should still detect malicious domains. But the highest-value control is to prevent the stolen credential from existing in the first place.

User Presence vs. User Verification

Two terms get mixed together too often:

Concept Plain-English meaning Why it matters
User presence The user touched the key, approved the prompt, or was physically involved. Helps prove that authentication was not entirely silent.
User verification The authenticator locally verified the user with a PIN, biometric, or equivalent method. Provides stronger assurance that the right person, not merely the right device, approved the login.

WebAuthn authenticator data includes flags for User Present and User Verified. For enterprise deployments, user verification should be required for normal workforce access and especially for privileged access.

Do not settle for “the device was there” when the workflow needs “the authorized user unlocked the credential.”

Attestation: Knowing What Created the Key

Attestation answers a simple question:

What kind of authenticator created this credential, and do we trust that model for this use case?

For broad workforce adoption, strict attestation may not always be required. Many consumer passkey providers do not expose the same provenance details, and requiring attestation everywhere can create adoption friction.

For privileged users, administrators, financial approvers, developers, security staff, and high-risk workflows, attestation becomes much more important. In those cases, the organization may want to allow only approved hardware security keys, approved device-bound passkeys, or approved enterprise passkey providers.

Microsoft Entra allows attestation enforcement at the passkey profile level. When attestation is enabled, only device-bound passkeys are allowed and synced passkeys are excluded. 

That is the correct direction for high-risk access.

Use convenience where the risk allows it. Use hardware-backed assurance where the blast radius demands it.

Synced Passkeys vs. Device-Bound Passkeys

Not all passkeys carry the same operational risk.

Type What it means Good fit Risk notes
Synced passkey The credential can be synced across devices through a passkey provider, such as an OS/cloud keychain or password manager. Standard workforce, lower-risk SaaS, broad adoption, BYOD-friendly scenarios. Better usability and recovery, but introduces sync-fabric, sharing, restore, and account-recovery risks.
Device-bound passkey The private key remains tied to one device or authenticator. Admins, executives, finance, developers, security teams, regulated workflows. Stronger control and provenance, but higher support cost and lockout risk.
Hardware security key A roaming authenticator, often USB/NFC/BLE, with keys protected in dedicated hardware. Highest-risk users, break-glass accounts, privileged access, financial approvals. Requires inventory, backup keys, training, and lifecycle management.

NIST allows syncable authenticators in applications seeking up to AAL2, but AAL3 requires a phishing-resistant authenticator with a non-exportable key. NIST explicitly says syncable authenticators cannot be used at AAL3 because their private keys are inherently exportable. 

That gives us a clean enterprise rule:

Use synced passkeys where usability and broad risk reduction matter most. Use device-bound credentials or hardware security keys where privilege, regulation, or business impact requires stronger assurance.

The Big Deployment Mistake: Turning On Passkeys and Declaring Victory

The wrong strategy is simple:

“We enabled passkeys. We are passwordless now.”

No.

A passkey project is not just an IdP configuration change. It is an identity modernization project.

The common failures are predictable:

  1. Weak fallback methods remain enabled.
  2. Recovery workflows become the new attack path.
  3. Privileged users are treated the same as standard users.
  4. Legacy applications keep password paths alive.
  5. Enrollment is not monitored.
  6. Exceptions never expire.
  7. Help desk processes are not hardened.
  8. Service accounts are ignored.
  9. Token theft and session abuse are treated as unrelated problems.

Passkeys reduce credential compromise risk. They do not solve endpoint malware, stolen browser sessions, OAuth abuse, SaaS misconfiguration, vulnerable internet-facing systems, malicious insiders, or weak vendor access.

Identity security is a system. Passkeys are one of the strongest components we have, but they still have to be engineered into the system.

Enterprise Implementation Methodology

The enterprise goal should be stated plainly:

Move the organization from password-centric authentication to phishing-resistant authentication while reducing weak fallback methods, hardening recovery, and tiering controls by risk.

Phase 0: Define Scope, Risk Tiers, and Target State

Start with decisions, not tools.

Decide:

  • Which IdP or IdPs are authoritative?
  • Which users are highest risk?
  • Which applications can use SSO?
  • Which applications support native WebAuthn/FIDO2?
  • Which workflows require phishing-resistant authentication immediately?
  • Which users may use synced passkeys?
  • Which users must use device-bound passkeys or hardware keys?
  • What fallback methods are acceptable during transition?
  • What is the exception process?
  • What is the recovery process?
  • What logs must be collected?
  • What metrics will leadership see?

Then build a risk-tier model.

Tier Examples Recommended approach
Tier 0 / highest privilege Global admins, domain admins, IdP admins, cloud admins, PAM admins, break-glass accounts. Two approved device-bound credentials or hardware security keys; attestation required where possible; no SMS, TOTP, or push fallback.
Tier 1 / high risk Executives, finance, HR, developers, help desk, security team, wire/ACH approvers. Device-bound preferred; synced allowed only with managed device and strong conditional access; hardened recovery.
Tier 2 / standard workforce General staff using SaaS and productivity apps. Synced or platform passkeys allowed; user verification required; backup method required before enforcement.
Tier 3 / frontline/shared device Kiosks, shared workstations, shift users. Hardware keys, badge-integrated FIDO, named-user access, or carefully designed shared-device strategy.
Third parties Vendors, contractors, MSPs. Require phishing-resistant MFA for privileged or sensitive access; enforce federation and conditional access.
Service accounts Non-human accounts, integrations, automations. Do not use passkeys. Use managed identities, workload identity federation, certificates, scoped tokens, vaulting, and rotation.

The biggest lesson: do not flatten the organization. A payroll clerk, a warehouse kiosk user, a cloud administrator, and a break-glass account do not carry the same risk.

Phase 1: Inventory Authentication Surfaces

Before enforcement, inventory where authentication actually happens.

Minimum fields should include:

  • Application or system name
  • Business owner
  • Authentication path
  • IdP integration
  • Current MFA methods
  • WebAuthn/FIDO2 support
  • SSO capability
  • User population
  • Privilege level
  • Recovery path
  • Logging source
  • Legacy protocols
  • Exception owner
  • Exception expiration date

Pay special attention to legacy authentication. Basic auth, old VPN flows, app passwords, IMAP/POP/SMTP AUTH, ROPC, local admin portals, unmanaged SaaS accounts, and shadow IdPs can quietly preserve the password attack surface after leadership thinks the problem is fixed.

This is where many “passwordless” projects fail. The modern front door gets hardened, but the side doors stay open.

Phase 2: Choose the Enterprise Passkey Architecture

Most organizations will deploy passkeys through their primary identity provider.

Microsoft Entra ID

Microsoft Entra supports passkeys using FIDO2/WebAuthn concepts and describes both device-bound passkeys and synced passkeys. Microsoft also recommends FIDO2 security keys for highly regulated industries or users with elevated privileges, while describing synced passkeys as a convenient, lower-cost option for most users outside highly regulated or sensitive contexts. 

A good Entra pattern usually includes:

  • Separate passkey profiles for standard users and privileged users.
  • Device-bound/security-key requirements for administrators.
  • Attestation enforcement for high-risk profiles where feasible.
  • Conditional Access authentication strengths.
  • Managed device requirements for sensitive access.
  • At least two authenticators enrolled before enforcement.
  • Removal of SMS, voice, TOTP, and push fallback for privileged users.
  • Logging of registration, removal, sign-in, recovery, and policy changes.

Google Workspace

Google Workspace administrators can allow users to skip password sign-in challenges and use a passkey covering first and second-factor authentication. Google also notes that administrators can restrict passkeys to hardware security keys only and can monitor passkey enrollment and usage through the security investigation tool. 

A good Google Workspace pattern usually includes:

  • Enabling skip-password capability by organizational unit.
  • Restricting hardware security keys for privileged OUs where required.
  • Confirming users have enrolled backup methods before enforcement.
  • Monitoring passkey enrollment and successful passkey sign-ins.
  • Removing weaker fallback for high-risk users.
  • Aligning device management and account recovery policies.

Okta

Okta describes Passkeys/FIDO2 WebAuthn and Okta FastPass as phishing-resistant authenticators and supports app sign-in policies that require phishing-resistant possession factors. Okta also logs phishing-resistant authentication events, including declined phishing attempts. 

A good Okta pattern usually includes:

  • Enabling Passkeys/FIDO2 WebAuthn and/or Okta FastPass.
  • Creating authenticator enrollment policies by risk group.
  • Requiring phishing-resistant authenticators for sensitive apps.
  • Using app sign-in policies rather than broad, one-size-fits-all rules.
  • Integrating managed device posture where available.
  • Alerting on enrollment changes, recovery activity, and phishing-resistant authentication failures.

Phase 3: Pilot With the People Who Can Break the Program Safely

Pilot with IT, security, identity administrators, help desk, a small executive group, finance users, mobile users, and a few users who are likely to have edge cases.

Test:

  • New device enrollment
  • Lost device recovery
  • Hardware key enrollment
  • Mobile sign-in
  • Cross-device sign-in
  • VPN access
  • SaaS access
  • Admin portal access
  • Password reset flows
  • Help desk identity verification
  • Offboarding
  • Break-glass access
  • Legacy application behavior
  • Logging and SIEM correlation
  • User communications

The pilot is not just about whether passkeys work. It is about whether the organization can support them without creating a weaker recovery path than the password path it replaced.

Phase 4: Roll Out by Risk, Not by Org Chart

The rollout sequence should be boring and deliberate:

  1. Identity administrators and security team.
  2. Cloud administrators and PAM administrators.
  3. Break-glass accounts.
  4. Finance, payroll, HR, executives, and developers.
  5. Help desk and support teams.
  6. General workforce.
  7. Third parties with privileged or sensitive access.
  8. Remaining business applications through SSO modernization.

Do not start with “everyone by Friday.” Start with the users whose compromise would hurt the most and whose workflows you can monitor carefully.

Phase 5: Harden Recovery, Lifecycle, and Monitoring

Attackers follow the path of least resistance.

If passkeys close the front door, attackers will look at recovery, registration, device replacement, and help desk exceptions.

Recovery controls should include:

  • Strong identity verification for authenticator reset.
  • Separate procedures for standard users and privileged users.
  • Two-person approval for privileged recovery.
  • Out-of-band callback using known-good contact information.
  • No recovery approval based solely on email access.
  • Logging and alerting for passkey addition, removal, reset, and recovery.
  • Time-bound temporary access.
  • Post-recovery review.
  • Executive reporting on recovery volume and exceptions.

NIST’s usability guidance explicitly calls out the need to provide users information about what to do if an authenticator is lost or stolen and to consider alternative authentication options for loss, damage, or availability issues. 

The enterprise interpretation is simple: do not enforce passkeys until recovery is engineered.

Policy Baseline Language

Here is a practical policy statement to adapt:

The organization will transition workforce authentication from password-centric methods to phishing-resistant authentication using passkeys based on FIDO2/WebAuthn. Standard users may use approved synced or device-bound passkeys. Privileged, administrative, financial, and other high-risk users must use approved device-bound passkeys or hardware security keys. Passwords, SMS OTP, voice OTP, email OTP, TOTP, and push approval may be used only as temporary transition or exception methods where explicitly risk-accepted. Account recovery, passkey registration, passkey removal, and fallback authentication are security-sensitive workflows and must be logged, monitored, and governed.

Minimum technical requirements:

Control Standard
User verification Required.
User presence Required where applicable.
Passkey count Minimum two approved authenticators per user before enforcement.
Admin authentication Device-bound FIDO2/security key; attestation preferred or required.
Standard workforce Synced or device-bound passkeys based on risk.
Shared accounts Prohibited where feasible; replace with named accounts and PAM.
Service accounts No passkeys; use workload identity or managed secrets.
Recovery Documented, verified, logged, and alert-generating.
Logging Registration, sign-in, failure, recovery, removal, device change, and admin changes.
Exceptions Time-bound, owner-assigned, and risk-accepted.

Enterprise Risk Register

Risk Probability Impact Mitigation
Weak fallback remains enabled High High Remove SMS/TOTP/push for admins first; enforce phishing-resistant authentication strength; maintain an exception register.
Help desk becomes the new attack path High High Require strong identity verification, callback procedures, two-person approval for privileged recovery, and recovery-event alerting.
Users lose access due to device loss Medium Medium Require two authenticators; issue backup keys for high-risk users; document recovery.
Synced passkeys are restored or shared to unmanaged devices Medium Medium/High Use managed profiles, MDM, device compliance, passkey provider controls, and device-bound keys for high-risk groups.
Legacy apps block enforcement High Medium/High Inventory apps, front with SSO, modernize authentication, isolate, or risk-accept temporarily.
Token theft bypasses authentication strength Medium High Use device compliance, session protection, continuous access evaluation, EDR, browser/session controls, and rapid revocation.
Attestation gaps create uncertainty Medium Medium Require attestation for privileged groups; use approved authenticator lists; allow non-attested only for lower-risk users.
BYOD creates inconsistent security posture Medium Medium Separate standard and high-risk use cases; require compliant devices for sensitive access.
Break-glass accounts remain password-only Medium High Use hardware keys, strong vaulting, monitoring, emergency access review, and tested procedures.
Users misunderstand biometrics Medium Low/Medium Explain that biometrics stay local and are not sent to the website, application, or employer.

A Practical 12-Month Roadmap

0–30 Days: Planning and Readiness

  • Define passkey policy and risk tiers.
  • Inventory applications and authentication paths.
  • Identify privileged and sensitive user groups.
  • Decide approved authenticator types.
  • Configure pilot policies in the IdP.
  • Draft help desk and recovery runbooks.
  • Prepare user communications.
  • Procure hardware security keys for administrators and high-risk users.

31–60 Days: Pilot

  • Enroll IT, security, and admin pilot users first.
  • Require at least two authenticators per pilot user.
  • Validate registration, sign-in, recovery, mobile, VPN, and legacy app behavior.
  • Run phishing-resistant authentication tests.
  • Tune SIEM alerts and help desk workflows.
  • Document blockers and exceptions.

61–90 Days: Privileged Enforcement

  • Require device-bound passkeys or hardware security keys for administrators.
  • Disable SMS, TOTP, and push fallback for admin accounts.
  • Require phishing-resistant authentication for IdP admin portals, cloud consoles, PAM, EDR, backup consoles, VPN admin access, finance approvals, and security tools.
  • Review break-glass accounts.
  • Begin executive and finance enrollment.

91–180 Days: Workforce Expansion

  • Enable passkey sign-in for all users.
  • Require two authenticators before enforcement.
  • Retire weak MFA for sensitive applications.
  • Move remaining password-based applications behind SSO where possible.
  • Track adoption metrics weekly.
  • Publish exceptions to leadership and security governance.

181–365 Days: Password Reduction and Optimization

  • Reduce password prompts.
  • Remove legacy authentication protocols.
  • Decommission app passwords and basic auth.
  • Expand phishing-resistant authentication to third parties.
  • Review account recovery events quarterly.
  • Run tabletop exercises and red-team simulations against recovery and fallback paths.
  • Add passkey support requirements to procurement and vendor risk management.

Metrics Leadership Should See

A passkey program needs measurement. Otherwise it becomes another “we turned it on” control.

Track:

  • Percent of users with at least one passkey.
  • Percent of users with at least two authenticators.
  • Percent of privileged users using device-bound credentials.
  • Password sign-ins by application.
  • Passkey sign-ins by application.
  • Failed passkey attempts.
  • Recovery events.
  • Passkey removals.
  • New authenticator registrations.
  • Weak MFA usage.
  • Exceptions by owner and expiration date.
  • Legacy authentication attempts.
  • High-risk users without compliant authentication.
  • Third-party users without phishing-resistant authentication.
  • Admin sign-ins that did not meet policy.

The dashboard should not be complicated. It should answer one question:

Are we actually reducing credential risk, or did we just add a new option?

What Passkeys Do Not Solve

This is the part vendors sometimes skip.

Passkeys do not fix:

  • Compromised endpoints.
  • Stolen session tokens.
  • Malware running in the user context.
  • OAuth consent abuse.
  • Overprivileged SaaS integrations.
  • Weak device management.
  • Poor logging.
  • Vulnerable internet-facing systems.
  • Help desk social engineering.
  • Weak account recovery.
  • Shared accounts.
  • Unmanaged vendor access.
  • Excessive privilege.
  • Poor offboarding.
  • Business process fraud.

That is not a criticism of passkeys. It is a reminder that identity security is layered.

Passkeys make it much harder to steal and replay credentials. That is a huge win. But attackers adapt. Once the password is gone, they will move toward recovery abuse, token theft, endpoint compromise, malicious OAuth grants, social engineering of support teams, and exploitation of systems that sit outside the modern IdP.

So build the rest of the program.

The Bottom Line

Passkeys are a major improvement because they remove the reusable password from the authentication ceremony.

They replace a shared secret with public-key cryptography, origin binding, local user verification, and challenge-response authentication. That is a structural improvement, not a cosmetic one.

But the right enterprise approach is not “turn on passkeys for everyone and declare victory.”

The right approach is:

  1. Use passkeys for broad workforce passwordless authentication.
  2. Use device-bound passkeys or hardware security keys for privileged and regulated users.
  3. Remove weak fallback methods.
  4. Harden recovery and lifecycle management.
  5. Measure adoption and residual risk.
  6. Tie identity hardening to endpoint security, session protection, vulnerability management, vendor access, and incident response.

Passkeys should be part of a rational identity security program.

Not hype.

Not magic.

Just better engineering.

More Information and Assistance

At MicroSolved, Inc., we help organizations move from security intentions to operational reality. Passkeys are a strong control, but the success of a passkey program depends on architecture, policy, implementation sequencing, recovery design, monitoring, and user communication.

MicroSolved can help your organization:

  • Assess your current authentication architecture.
  • Inventory password, MFA, SSO, and legacy authentication paths.
  • Build a passkey deployment roadmap.
  • Define risk tiers for standard, privileged, executive, financial, developer, and third-party users.
  • Design policy for synced passkeys, device-bound passkeys, and hardware security keys.
  • Harden account recovery and help desk workflows.
  • Configure SIEM monitoring and identity alerts.
  • Test fallback paths through tabletop exercises and adversarial simulations.
  • Build executive dashboards for identity risk reduction.
  • Integrate phishing-resistant authentication into broader security governance.

If you are planning a passkey rollout, struggling with legacy authentication, or unsure how to reduce password risk without creating new recovery risk, reach out to MicroSolved, Inc. We would be glad to help you think it through.

Contact MicroSolved at +1.614.351.1237 or info@microsolved.com.

Relax. We’re on watch.


References

  • FIDO Alliance — Passkeys and passwordless authentication. 
  • W3C — Web Authentication: An API for accessing Public Key Credentials, Level 3. 
  • NIST SP 800-63B — Authentication and Lifecycle Management. 
  • Microsoft Learn — Passkeys/FIDO2 authentication in Microsoft Entra ID. 
  • Google Workspace Admin Help — Allow users to skip passwords at sign-in. 
  • Okta Help — Phishing-resistant authentication. 
  • Microsoft Digital Defense Report 2025. 
  • Verizon 2026 Data Breach Investigations Report. 

AI tools were used as a research assistant for this content, but human moderation and writing are also included. Images are AI-generated.

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AI Agents Are Already Working for You. Who’s Managing Them?

AI Agents Are Not Applications. They Are Digital Workers.

Most organizations are adopting AI agents faster than they are learning how to govern them.

That is the problem.

A chatbot that answers questions is one thing. An AI agent that can access business data, use tools, trigger workflows, generate artifacts, make recommendations, or alter enterprise state is something else entirely.

At that point, the organization is no longer just deploying software.

It is introducing a new kind of operational actor.

That actor needs identity.

It needs boundaries.

It needs oversight.

It needs evidence.

It needs a human owner.

It needs a kill switch.

In other words, AI agents must be managed more like digital workers than ordinary applications.

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Why My AI Agents Needed CaneCorso as a Security Control Plane

AI agents are powerful because they can read, reason, summarize, decide, and act across a wide range of information sources.

That is also what makes them dangerous.

The more useful an agent becomes, the more likely it is to consume data I do not fully trust. Emails. Newsletters. RSS feeds. API responses. Documents sent as attachments. Social media. YouTube transcripts. Scraped search results. Web pages. Translated content. Random bits of text pulled from places where I do not control the author, the formatting, the intent, or the payload.

That is a very different security model than the one most of us are used to.

In traditional applications, we spend a lot of time separating code from data, users from administrators, trusted networks from untrusted networks, and internal systems from the internet. With LLMs and agents, all of those boundaries start to blur. Instructions, context, content, and intent all arrive in the same stream. The model has to reason over that stream, and the agent has to decide what to do with the result.

That is exactly why I wanted a security control plane in front of my own AI agents.

For me, that control plane became CaneCorso™.

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CaneCorso™ and the Real Problems AI Is Creating for the Business

AI didn’t sneak into the enterprise.

It walked in through productivity.

Email triage. Document handling. Support workflows. Internal copilots. Retrieval systems. Early agentic use cases. All of it made sense at the time. All of it still does.

But something changed along the way.

We didn’t just adopt AI—we embedded it into workflows that can influence decisions, expose data, and take action.

That’s where the problem starts.

And it’s exactly where CaneCorso™ is designed to operate.

CaneCorsoAI


AI Risk Isn’t a Model Problem — It’s a Workflow Problem

There’s a persistent misunderstanding in the market right now.

Most conversations about AI security still center on the model—what it knows, how it behaves, whether it can be tricked.

That’s not where the real risk lives.

The real risk shows up when:

  • Untrusted content enters a workflow
  • That workflow uses AI to interpret or transform it
  • And the output influences business operations

That content might come from:

  • Email
  • Documents
  • OCR pipelines
  • Retrieved knowledge (RAG)
  • Support tickets
  • External data sources

Once it’s in the workflow, it’s no longer just data.

It’s influence.

CaneCorso™ exists to control that influence—before it becomes an operational problem.

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Introducing CaneCorso: An AI Application Firewall Built for Real Workflows

AI has officially crossed the line from experiment to infrastructure.

Email flows into copilots. Documents feed RAG pipelines. Support tickets trigger agents that can take action. The convenience is real—and so is the risk.

What hasn’t caught up is security.

Most security models were built for a world where inputs were predictable and trust boundaries were well-defined. That world doesn’t exist anymore. Today, untrusted content flows directly into systems that can reason, decide, and act.

That’s exactly where things get interesting—and dangerous.


When Good Data Carries Bad Instructions

One of the biggest misconceptions about AI security is that it’s a model problem. It’s not. It’s a workflow problem.

Attackers don’t need to break in anymore. They ride along with legitimate data—emails, PDFs, tickets, knowledge base entries—and inject instructions that your AI system may interpret as truth.

Think about what that means in practice:

  • A support ticket that contains hidden instructions
  • A PDF with embedded prompt injection
  • A knowledge base entry that poisons RAG outputs
  • An approval workflow manipulated through summarization

Layer in human behavior—blind trust, over-privileged access, weak validation—and you’ve got a system primed to fail in ways that traditional controls simply won’t catch.

CaneCorsoAI


A More Rational Approach to AI Security

CaneCorso™ takes a different path.

Instead of trying to block everything suspicious (and breaking workflows in the process), it follows what’s described in the Rational AI Security model —security that behaves more like an immune system than a wall.

That means:

  • Detecting and isolating threats without stopping the system
  • Treating all inbound content as untrusted by default
  • Preserving business continuity while reducing risk
  • Producing measurable, auditable outcomes

This isn’t theoretical. It’s a direct response to how AI systems actually behave in production.


One Control Plane for AI Workflows

At its core, CaneCorso gives you a shared AI Application Firewall—a single control plane that sits between your workflows and your models.

Instead of every team building its own brittle filters, you get consistent, reusable protection across:

  • Email triage and analysis
  • RAG pipelines and knowledge systems
  • Document AI and OCR ingestion
  • Support and ticketing workflows
  • Agent-driven automation

The platform delivers:

  • Runtime decisions: allow, sanitize, tokenize, or block
  • Privacy controls: redact or tokenize sensitive data before model exposure
  • Audit-ready logs: reasons, scores, and evidence you can actually use
  • Adversarial validation: Injection Scanner proves controls before and after deployment

This isn’t just about stopping attacks—it’s about making security operationally usable.

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Rethinking Account Lockouts: Why 15 Minutes Isn’t a Strategy

There’s a moment in almost every security program where someone asks a deceptively simple question:

“Is 15 minutes a standard account lockout duration?”

The short answer? No.
The more honest answer? It’s common—but often wrong for the environment it’s deployed in.

And I’ve seen more than a few organizations learn that the hard way.

3Errors


The Myth of the “Standard” Lockout

If you go looking for authoritative guidance—from Center for Internet SecurityFFIEC, or CISA—you’ll notice something interesting:

They don’t tell you what number to use.

Instead, they consistently emphasize:

  • Risk-based decision making
  • Balancing usability and security
  • Detecting and responding to threats—not just blocking them

That’s not an accident. It’s an acknowledgment that static controls like lockouts are blunt instruments in a very dynamic threat landscape.


What We Actually See in the Real World

Across environments—financial services, healthcare, SaaS, manufacturing—the patterns are pretty consistent:

Setting Typical Range
Failed attempts before lockout 3–10
Lockout duration 5–30 minutes
Most common default 10–15 minutes

So yes, 15 minutes sits comfortably in the middle.

But “common” and “effective” are not the same thing.


Where 15 Minutes Breaks Down

1. It Punishes Users More Than Attackers

A 15-minute lockout sounds reasonable—until you multiply it.

  • A clinician locked out mid-shift
  • A call center agent missing SLAs
  • A trader unable to access systems during market hours

Now multiply that by repeated lockouts from cached credentials, mobile devices, or service accounts.

You don’t just have a security control—you have an operational problem.


2. It Doesn’t Stop Modern Attacks

Attackers have evolved. Most environments haven’t.

Today’s common attack patterns:

  • Password spraying (low-and-slow, avoids thresholds)
  • Credential stuffing (valid credentials, no lockout triggered)

A longer lockout duration doesn’t meaningfully impact either.

If anything, it gives a false sense of security while the real attack path goes untouched.


What Actually Works: A Layered Approach

This is where the conversation needs to shift—from “what’s the right number?” to “what’s the right strategy?”

1. Lockouts Are Supporting Controls—Not Primary Defenses

If you’re relying on lockouts as your main protection, you’re already behind.

At a minimum, you should be pairing with:

  • MFA everywhere it’s technically feasible
  • Conditional access (device, location, behavior)
  • Authentication throttling and smart detection

2. Tune for Risk, Not Defaults

A more balanced configuration tends to look like:

  • 5–10 failed attempts
  • 5–10 minute lockout
  • Reset counter after a defined cooldown window

This reduces user friction while still slowing down brute-force attempts.

More importantly—it acknowledges that lockouts are a speed bump, not a wall.


3. Progressive Delays Beat Hard Lockouts

One of the most underutilized strategies is progressive delay:

  • Attempts 1–2 → no delay
  • Attempts 3–5 → 30–60 second delay
  • Continued attempts → increasing delay

This approach:

  • Degrades attacker efficiency
  • Preserves user productivity
  • Avoids helpdesk spikes

It’s a far more surgical control than a blanket 15-minute lockout.


4. Detection Over Punishment

Modern security programs don’t just block—they observe.

You should be:

  • Logging all failed authentication attempts
  • Alerting on patterns (spraying, geographic anomalies)
  • Correlating identity signals across systems

Lockouts should be one signal among many—not the primary response.


Implementing This in Active Directory

Let’s get practical.

In on-prem Active Directory, you’re working primarily with Group Policy.

Recommended Baseline

In your domain or fine-grained password policy:

  • Account lockout threshold: 5–10 attempts
  • Account lockout duration: 5–10 minutes
  • Reset account lockout counter after: 10–15 minutes

Where to Configure

  • Group Policy Management Console (GPMC)
    • Computer Configuration → Policies → Windows Settings → Security Settings → Account Policies → Account Lockout Policy

Advanced Considerations

  • Use Fine-Grained Password Policies (FGPP) for high-risk accounts (admins, service accounts)
  • Monitor Event IDs:
    • 4625 (failed logon)
    • 4740 (account locked out)
  • Feed logs into your SIEM for correlation and alerting

Implementing This in Microsoft 365

In Microsoft 365, the model shifts significantly.

You don’t directly control “lockout duration” in the same way—because the platform is already applying smart lockout behavior.

Smart Lockout (Azure AD / Entra ID)

  • Automatically tracks failed attempts
  • Uses adaptive thresholds
  • Differentiates between familiar and unfamiliar locations

What You Should Do Instead

1. Enable and Enforce MFA

  • Conditional Access → Require MFA for all users (with staged rollout if needed)

2. Configure Conditional Access Policies

  • Block legacy authentication
  • Require compliant devices
  • Apply geographic restrictions where appropriate

3. Monitor Identity Signals

  • Azure AD Sign-in logs
  • Risky sign-ins and users
  • Integration with Defender for Identity / Sentinel

4. Tune Smart Lockout (if needed)

  • Default threshold is typically sufficient
  • Adjust only if you have a strong operational reason

The Bottom Line

A 15-minute lockout isn’t wrong.

It’s just incomplete.

  • ✔️ It’s common
  • ❌ It’s not a standard
  • ⚠️ It can create more operational pain than security value

The real shift is this:

Stop treating account lockouts as a primary control. Start treating them as part of a layered identity defense strategy.

Because in today’s environment, the goal isn’t just to block access.

It’s to understand it.

 

 

* AI tools were used as a research assistant for this content, but human moderation and writing are also included. The included images are AI-generated.

Update on PromptDefense Suite and AI Security Research

Last week, I discussed why and some of how we built the new PromptDefense Suite

This week, we are discussing the product’s future internally and how we might go to market. This is mainly due to two new capabilities we have built into the product. 

The first is an API and workflow automation mechanism. This allows organizations to stand up a single instance of PromptDefense and then use it to protect multiple AI/agent workflows. The code no longer has to be embedded directly in the project; instead, all defensive capabilities and logging can be accessed via an API instance. The API is robust and supports API key restrictions that tie into a rules engine, so that different workflows can have different trust models and actions pre-assigned in an audit-friendly way. 

Secondly, we have developed a licensing mechanism that covers protected workflows and skips the per-seat, per-token models that seemed too confusing for most firms looking for these kinds of tools. They told us they wanted a simpler licensing approach, and we developed a new licensing mechanism to make it easy, manageable, and auditable. Our testers have been calling it a win! 

As we continue with the beta-testing process and lock down our decisions about where the product is going, the news that drove us to create it continues to flow in. More of our clients are working on agents and AI-integrated workflows, which require this level of protection. While we continue to develop PromptDefender, we are also working to develop and release extended frameworks for AI model, agent, and product management, along with policies, procedures, and vendor risk assessment tools for these frameworks, for our vCISO clients. We’re also busy researching ongoing compliance implementation for AI workflows and agents, and should have more on that shortly. 

In the meantime, if you want to discuss AI or agent security, risk management, or other relevant topics, please reach out. We would love to talk with you and help align our modernization capabilities with your emerging needs. You can always email us at info@microsolved.com or call us at +1-614-351-1237. 

As always, thanks for reading. Stay safe out there, and stay tuned for more updates. 

From Alert Volume to Signal Yield: An Economic Framework for Measuring SOC Effectiveness

Six months after a major alert-reduction initiative, a SOC director proudly reports a 42% decrease in daily alerts. The dashboards look cleaner. The queue is shorter. Analysts are no longer drowning.

Leadership applauds the efficiency gains.

Then reality intervenes.

A lateral movement campaign goes undetected for weeks. Analyst burnout hasn’t meaningfully declined. The cost per incident response remains stubbornly flat. And when the board asks a simple question — “Are we more secure now?” — the answer becomes uncomfortable.

Because while alert volume decreased, risk exposure may not have.

This is the uncomfortable truth: alert volume is a throughput metric. It tells you how much work flows through the system. It does not tell you how much value the system produces.

If we want to mature security operations beyond operational tuning, we need to move from counting alerts to measuring signal yield. And to do that, we need to treat detection engineering not as a technical discipline — but as an economic system.

AppSec


The Core Problem: Alert Volume Is a Misleading Metric

At its core, an alert is three things:

  1. A probabilistic signal.

  2. A consumption of analyst time.

  3. A capital allocation decision.

Every alert consumes finite investigative capacity. That capacity is a constrained resource. When you generate an alert, you are implicitly allocating analyst capital to investigate it.

And yet, most SOCs measure success by reducing the number of alerts generated.

The second-order consequence? You optimize for less work, not more value.

When organizations focus on alert reduction alone, they may unintentionally optimize for:

  • Lower detection sensitivity

  • Reduced telemetry coverage

  • Suppressed edge-case detection

  • Hidden risk accumulation

Alert reduction is not inherently wrong. But it exists on a tradeoff curve. Lower volume can mean higher efficiency — or it can mean blind spots.

The mistake is treating volume reduction as an unqualified win.

If alerts are investments of investigative time, then the right question isn’t “How many alerts do we have?”

It’s:

What is the return on investigative time (ROIT)?

That is the shift from operations to economics.


Introducing Signal Yield: A Pareto Model of Detection Value

In most mature SOCs, alert value follows a Pareto distribution.

  • Roughly 20% of alert types generate 80% of confirmed incidents.

  • A small subset of detections produce nearly all high-severity findings.

  • Entire alert families generate near-zero confirmed outcomes.

Yet we often treat every alert as operationally equivalent.

They are not.

To move forward, we introduce a new measurement model: Signal Yield.

1. Signal Yield Rate (SYR)

SYR = Confirmed Incidents / Total Alerts (per detection family)

This measures the percentage of alerts that produce validated findings.

A detection with a 12% SYR is fundamentally different from one with 0.3%.

2. High-Severity Yield

Critical incidents / Alert type

This isolates which detection logic produces material risk reduction — not just activity.

3. Signal-to-Time Ratio

Confirmed impact per analyst hour consumed.

This reframes alerts in terms of labor economics.

4. Marginal Yield

Additional confirmed incidents per incremental alert volume.

This helps determine where the yield curve flattens.


The Signal Yield Curve

Imagine a curve:

  • X-axis: Alert volume

  • Y-axis: Confirmed incident value

At first, as coverage expands, yield increases sharply. Then it begins to flatten. Eventually, additional alerts add minimal incremental value.

Most SOCs operate blindly on this curve.

Signal yield modeling reveals where that flattening begins — and where engineering effort should be concentrated.

This is not theoretical. It is portfolio optimization.


The Economic Layer: Cost Per Confirmed Incident

Operational metrics tell you activity.

Economic metrics tell you efficiency.

Consider:

Cost per Validated Incident (CVI)
Total SOC operating cost / Confirmed incidents

This introduces a critical reframing: security operations produce validated outcomes.

But CVI alone is incomplete. Not all incidents are equal.

So we introduce:

Weighted CVI
Total SOC operating cost / Severity-weighted incidents

Now the system reflects actual risk reduction.

At this point, detection engineering becomes capital allocation.

Each detection family resembles a financial asset:

  • Some generate consistent high returns.

  • Some generate noise.

  • Some consume disproportionate capital for negligible yield.

If a detection consumes 30% of investigative time but produces 2% of validated findings, it is an underperforming asset.

Yet many SOCs retain such detections indefinitely.

Not because they produce value — but because no one measures them economically.


The Detection Portfolio Matrix

To operationalize this, we introduce a 2×2 model:

  High Yield Low Yield
High Volume Core Assets Noise Risk
Low Volume Precision Signals Monitoring Candidates

Core Assets

High-volume, high-yield detections. These are foundational. Optimize, maintain, and defend them.

Noise Risk

High-volume, low-yield detections. These are capital drains. Redesign or retire.

Precision Signals

Low-volume, high-yield detections. These are strategic. Stress test for blind spots and ensure telemetry quality.

Monitoring Candidates

Low-volume, low-yield. Watch for drift or evolving relevance.

This model forces discipline.

Before building a new detection, ask:

  • What detection cluster does this belong to?

  • What is its expected yield?

  • What is its expected investigation cost?

  • What is its marginal ROI?

Detection engineering becomes intentional investment, not reactive expansion.


Implementation: Transitioning from Volume to Yield

This transformation does not require new tooling. It requires new categorization and measurement discipline.

Step 1 – Categorize Detection Families

Group alerts by logical family (identity misuse, endpoint anomaly, privilege escalation, etc.). Avoid measuring at individual rule granularity — measure at strategic clusters.

Step 2 – Attach Investigation Cost

Estimate average analyst time per alert category. Even approximations create clarity.

Time is the true currency of the SOC.

Step 3 – Calculate Yield

For each family:

  • Signal Yield Rate

  • Severity-weighted yield

  • Time-adjusted yield

Step 4 – Plot the Yield Curve

Identify:

  • Where volume produces diminishing returns

  • Which families dominate investigative capacity

  • Where engineering effort should concentrate

Step 5 – Reallocate Engineering Investment

Focus on:

  • Improving high-impact detections

  • Eliminating flat-return clusters

  • Re-tuning threshold-heavy anomaly models

  • Investing in telemetry that increases high-yield signal density

This is not about eliminating alerts.

It is about increasing return per alert.


A Real-World Application Example

Consider a SOC performing yield analysis.

They discover:

  • Credential misuse detection: 18% yield

  • Endpoint anomaly detection: 0.4% yield

  • Endpoint anomaly consumes 40% of analyst time

Under a volume-centric model, anomaly detection appears productive because it generates activity.

Under a yield model, it is a capital drain.

The decision:

  • Re-engineer anomaly thresholds

  • Improve identity telemetry depth

  • Increase focus on high-yield credential signals

Six months later:

  • Confirmed incident discovery increases

  • Analyst workload becomes strategically focused

  • Weighted CVI decreases

  • Burnout declines

The SOC didn’t reduce alerts blindly.

It increased signal density.


Third-Order Consequences

When SOCs optimize for signal yield instead of alert volume, several systemic changes occur:

  1. Board reporting becomes defensible.
    You can quantify risk reduction efficiency.

  2. Budget conversations mature.
    Funding becomes tied to economic return, not fear narratives.

  3. “Alert theater” declines.
    Activity is no longer mistaken for effectiveness.

  4. Detection quality compounds.
    Engineering effort concentrates where marginal ROI is highest.

Over time, this shifts the SOC from reactive operations to disciplined capital allocation.

Security becomes measurable in economic terms.

And that changes everything.


The Larger Shift

We are entering an era where AI will dramatically expand alert generation capacity. Detection logic will become cheaper to create. Telemetry will grow.

If we continue to measure success by volume reduction alone, we will drown more efficiently.

Signal yield is the architectural evolution.

It creates a common language between:

  • SOC leaders

  • CISOs

  • Finance

  • Boards

And it elevates detection engineering from operational tuning to strategic asset management.

Alert reduction was Phase One.

Signal economics is Phase Two.

The SOC of the future will not be measured by how quiet it is.

It will be measured by how much validated risk reduction it produces per unit of capital consumed.

That is the metric that survives scrutiny.

And it is the metric worth building toward.

 

 

* AI tools were used as a research assistant for this content, but human moderation and writing are also included. The included images are AI-generated.