When identity verification ends at login, trust becomes the vulnerability attackers exploit. Deepfake impersonation, synthetic identities, and AI-powered social engineering are exposing the limits of traditional IAM models. Consltek’s Deepfake to Breach: SMB Playbook for Identity Attacks helps organizations understand how identity threats evolve beyond authentication—and what practical defenses reduce exposure.

The Specific Attack Pattern That Continuous Verification Addresses

Understanding why continuous verification changes the security equation requires understanding exactly how AI-powered identity attacks exploit point-in-time authentication in practice.

The most commercially damaging deepfake attack scenarios in enterprise environments do not typically involve replacing an identity at the authentication layer, where detection controls are increasingly present. They involve joining or entering a verified interaction as a legitimate participant and then introducing a synthetic identity element after authentication has already been completed and security attention has moved elsewhere.

A verified executive joins a board meeting or investor call legitimately. Mid-session, a face-swap or voice-cloning element replaces or supplements their authentic presence. The session is already authenticated. The existing participants have already made the trust judgments that authentication is designed to support. The synthetic substitution occurs in a context where no one is looking for it because the verification check that would catch it only happened at entry.

The financial fraud version of this pattern, an authenticated individual in a wire transfer authorization call being partially or fully replaced by a synthetic voice or video after initial verification, is the attack scenario behind a significant portion of the $1.6 billion in 2025 deepfake losses. The enterprise has verified who joined the call. It has not verified who is speaking when the authorization instruction is given.

GetReal Protect’s continuous verification model addresses this by maintaining verification throughout the session rather than only at entry. If the face on the screen no longer matches the enrolled identity, if audio shifts to synthetic speech, or if face-swap or morphing manipulation is detected at any point during the interaction, the platform surfaces it to the security team immediately. The verification is not a checkpoint. It is a continuous thread.

Four Integrated Capabilities and Why Integration Matters

The platform’s four capabilities, deepfake detection, impersonation detection, continuous identity verification, and global threat intelligence, are individually available through various point solutions in the market. The integration of all four into a single platform with continuous monitoring across the full session lifecycle is what differentiates GetReal Protect from the assembled capabilities that most enterprise security programs are currently using to address AI identity fraud.

Deepfake detection answers whether the person on the call is authentic or synthetically generated and whether any real-time manipulation is occurring. This is the detection layer that identifies AI-generated content and live face-swap or voice-cloning activity.

Impersonation detection answers whether the person presented matches the claimed identity, comparing what the platform sees in the session against enrolled biometric templates. A deepfake that successfully mimics the appearance of a real person may pass authenticity checks while failing impersonation detection if the biometric match against the enrolled template is outside acceptable parameters.

Continuous identity verification answers whether the match persists throughout the session. This is the capability that closes the post-authentication substitution gap, maintaining the identity thread from session entry through every subsequent moment of interaction.

Global threat intelligence answers whether any identities in the session are known threat actors or individuals with elevated fraud risk profiles. This layer provides the contextual risk intelligence that makes verification findings actionable rather than simply observational.

The combination of these four capabilities across the full session lifecycle is what converts identity verification from a point-in-time access control mechanism into a continuous trust assurance system. For enterprise organizations managing high-value digital interactions including M&A discussions, financial authorizations, executive communications, and sensitive operational decisions, that continuous assurance is the security model that the current threat environment requires.

The Integration Architecture and Why Frictionless Deployment Matters

GetReal Protect’s integration with Microsoft Teams, Cisco Webex, Zoom, and voice systems, alongside over 40 native integrations including Okta, Microsoft Entra, and CyberArk, addresses a deployment reality that has historically limited adoption of enterprise security controls in the collaboration environment.

Security tools that require participants to install separate applications, use specific approved platforms, or take additional verification steps during already complex digital interactions generate user resistance that limits adoption, encourages workarounds, and ultimately produces inconsistent coverage across the interaction population the tool is meant to protect. In high-stakes executive and financial contexts where the participants may include external counterparties, board members, investors, and regulatory personnel, the ability to impose security tool requirements is limited by relationship and protocol constraints that security teams cannot override.

GetReal Protect’s consent-based enrollment model, where verification becomes automatic and frictionless once enrollment is complete, with no additional action required from IT teams, hosts, or participants, addresses this adoption barrier directly. Enrolled participants are verified continuously without any visible interaction overhead. Uninvited synthetic identities that have not been enrolled and cannot produce matching biometric signals are surfaced as anomalies rather than requiring users to actively flag suspicious behavior.

The 40-plus native integrations with existing IAM frameworks mean the platform complements rather than replaces the identity infrastructure that enterprise security programs have already invested in. GetReal Protect operates as an additional continuous verification layer on top of existing Okta, Entra, and CyberArk deployments, extending those platforms’ reach into the live session context that conventional IAM tools exit at the point of authentication.

The Privacy Architecture That Enterprise Procurement Requires

The continuous biometric monitoring that GetReal Protect deploys across enterprise collaboration sessions creates a privacy governance requirement that, if not addressed correctly, would make the platform commercially unviable in most regulated enterprise environments regardless of its security capabilities.

GetReal Security‘s privacy architecture reflects an understanding of this requirement. Verification does not occur unless participants provide explicit consent. Data is owned by the individual and not shared externally, with GetReal acting as custodian on behalf of enterprise customers. Enrollees retain the right to revoke consent and request data removal at any time.

The compliance framework supporting this architecture includes SOC 2 Type II certification, GDPR compliance, CCPA and CPRA compliance, and BIPA compliance. BIPA, the Illinois Biometric Information Privacy Act, is specifically relevant because it is the most stringent state-level biometric privacy regulation in the United States and has been the basis for significant class action litigation against organizations that collected biometric data without appropriate consent frameworks. GetReal’s explicit BIPA compliance signals that the platform has been designed to meet the highest current standard of biometric privacy governance, which provides procurement teams in regulated industries with meaningful compliance assurance.

For enterprise legal, compliance, and HR teams that will be involved in any procurement decision involving continuous biometric monitoring of employees and external participants, the consent-first architecture and explicit regulatory compliance framework are not ancillary features. They are the enabling conditions that make the platform deployable in the organizational and regulatory contexts where deepfake identity fraud risk is highest.

The Financial Sector Urgency and the Broader Enterprise Risk Profile

The deepfake fraud statistics underlying this announcement warrant direct examination because they describe a threat trajectory that has broken from the trend line that enterprise security programs have been using to calibrate their investment urgency.

The comparison between $1.6 billion in deepfake-driven financial losses in 2025 alone and $130 million in cumulative losses across the entire 2019 to 2023 period is not a marginal increase. It is a category-level escalation that reflects the commercial availability of high-quality voice cloning and video deepfake tools, the organizational capability of fraud networks to deploy them at scale, and the absence of adequate detection controls in the enterprise collaboration environments where the most valuable digital interactions occur.

The eightfold growth in synthetic identity fraud in 2025 reported by LexisNexis provides corroborating data from a different research methodology. Two independent data sources showing comparable magnitude of growth in AI-powered identity fraud during the same period describe a documented market condition rather than a single-source anomaly.

For CISOs and enterprise risk leaders building the internal case for continuous identity verification investment, those figures provide the financial exposure anchor that executive and board-level conversations require. The $1.6 billion in 2025 losses represents an industry-wide figure that includes organizations with security programs considerably less sophisticated than the enterprises that would be evaluating GetReal Protect. The specific loss exposure for any individual enterprise will depend on the volume and value of digital interactions that are vulnerable to deepfake substitution, but the industry trend makes the directional risk argument accessible without requiring internal incident data.

The Pre-Ground Check Concept and Its Strategic Security Implications

CEO Matt Moynahan’s framing of the platform’s philosophy, reversing the background check concept into a pre-ground check where identity comes first, describes a meaningful reorientation of how enterprise security programs approach identity assurance in digital interactions.

Traditional background checks verify a person’s history before granting access or establishing a relationship. That model assumes the person being checked is who they claim to be, a foundational assumption that AI-powered synthetic identity creation has made unreliable. A background check on a synthetic identity built on real personal data may return clean results while confirming nothing about the actual identity of the person presenting it.

A pre-ground check model, where continuous verification establishes and maintains the authenticity of the identity throughout the interaction rather than relying on historical records to validate a claimed identity at entry, addresses this gap at the right architectural layer. Identity should come first and should persist throughout every digital interaction rather than being established at entry and assumed to be maintained thereafter.

For enterprise security architects designing identity assurance frameworks for high-value digital interactions, that philosophical reorientation has practical implications for how continuous verification capability is positioned within the broader identity security program. It is not an addition to existing authentication controls. It is the control that closes the gap between what authentication verifies and what actually matters in an environment where sophisticated AI can substitute for authenticated identities after verification has already occurred.

Market Signals and Competitive Positioning

The general availability of GetReal Protect’s continuous identity verification creates a defined commercial category that did not exist in mature enterprise security buying before this announcement. Point-in-time deepfake detection has been available through various vendors. Continuous verification across voice and video throughout the full session lifecycle, integrated with enterprise IAM frameworks and collaboration platforms, has not been commercially available at enterprise scale prior to this launch.

That category-creation dynamic carries both an opportunity and an adoption challenge. Enterprise security buyers evaluating an entirely new product category require more evidence of operational performance and organizational fit than they require for established categories with comparative vendor options. The consent-based privacy architecture, SOC 2 Type II certification, and explicit regulatory compliance framework that GetReal has embedded in the platform are designed in part to address the organizational trust requirements that category-creation adoption demands.

The integration with enterprise collaboration platforms that security buyers are already managing, Teams, Webex, Zoom, and existing IAM vendors, reduces the deployment friction that new category adoption typically encounters. Security leaders who can frame the GetReal Protect deployment as an extension of their existing collaboration and identity security investments rather than an independent new product evaluation will find the internal procurement path more accessible than those presenting it as a standalone new technology category acquisition.

The organizations most likely to accelerate through the procurement process are those that have already experienced a deepfake-related incident or near-miss, those operating in financial services where the regulatory and fiduciary consequences of deepfake fraud are most acute, and those managing executive communications or board-level digital interactions where the value of the interactions being protected justifies the investment urgency. For all three populations, the $1.6 billion industry loss figure and the eightfold fraud growth trajectory provide the external validation that internal incident data alone may not yet supply.