5 Strategies for Improving AI Readiness in Identity Security

To help companies remain competitive amidst changing markets, the Solutions Review editors are exploring how companies can improve the AI readiness of their identity security processes and technologies.

The continued convergence of artificial intelligence and identity security represents one of the most significant paradigm shifts in cybersecurity since the advent of zero-trust architecture. Yet most organizations remain trapped in legacy thinking, often treating AI as another tool to bolt onto existing identity frameworks instead of recognizing it as a fundamental reimagining of how identity verification, authentication, and access control must evolve.

Consider the asymmetric threat environment we now inhabit: while security teams manually review access logs and conduct quarterly access audits, AI-powered attackers can execute thousands of credential stuffing attempts per second, synthesize convincing deepfake authentication, and adapt their attack strategies in real-time. The results from CrowdStrike’s 2025 Global Threat Report confirm this by showing that 79 percent of detections were malware-free, which indicates that valid credentials were used for unauthorized entry. Organizations that fail to prioritize AI readiness in their identity security posture won’t just fall behind, but find themselves incompatible with the emerging threat landscape.

Avoiding that disaster will require more than incremental improvements to existing systems. There’s too much at stake for that level of patience, and threat actors won’t wait while your company gradually bolsters its defenses. Organizations that recognize this imperative and act decisively will create compounding advantages that become insurmountable over time. With that in mind, the Solutions Review editors have compiled a few ways companies of all sizes can prioritize the AI readiness of their identity security strategies.

1) Implement Continuous Behavioral Biometrics with Multimodal AI Fusion

Traditional multi-factor authentication is already becoming obsolete in an AI-dominated threat environment, and with over 90 percent of companies experiencing identity-related incidents on an annual basis, the need for stronger human and non-human identity security has never been higher.

One alternative to MFA is continuous behavioral biometrics powered by multimodal AI systems, which can create dynamic identity signatures that combine keystroke dynamics, mouse movement patterns, gait analysis from mobile sensors, voice stress analysis, and micro-facial expression detection. This approach isn’t about improving accuracy in individual biometric modalities, but about deploying AI models that excel at detecting anomalies in the relationships between modalities. For example, even if a sophisticated attacker can replicate your typing pattern, they can’t also replicate the micro-correlations between your typing rhythm and subtle head movements while concentrating.

Additionally, the technical imperative requires moving from simple rule-based systems to deep learning architectures capable of understanding the complex interplay between different behavioral modalities. Organizations must build federated learning networks that allow behavioral models to improve themselves without exposing raw biometric data. This creates a competitive moat while addressing privacy concerns that will become increasingly important as regulatory frameworks evolve.

2) Deploy Adversarial Identity Intelligence with Predictive Threat Modeling

Most identity security systems are still reactive, capable only of responding to threats after they’ve penetrated initial defenses. This approach is not only inadequate in the current threat landscape but actively counterproductive. When AI-powered attacks are evolving faster than human security teams can adapt, they need technology to help them catch up, and, for the most part, the tools available to them are coming up short. If a company wants to future-proof its identity security, it must double down on AI readiness. This means shifting from a defensive posture to an offensive one that deploys AI systems that can model potential attack vectors against their specific identity infrastructure, simulate thousands of attack scenarios daily, and identify vulnerabilities before adversaries do.

The reality is that traditional penetration testing has become counterproductive, as human-led security assessments cannot match the speed and creativity of AI-powered attacks. Organizations should transition to AI vs. AI security validation, where adversarial neural networks continuously probe defenses while defensive AI systems adapt in real-time. This requires creating “digital twins” of identity ecosystems that run parallel to production environments, executing AI-generated attack simulations alongside machine learning models that analyze results to predict where real attacks are most likely to succeed.

According to research from EY, AI has become part of 59 percent of all cyber patents, making it the primary technology explored in cyber-centric research since 2017. That’s an encouraging statistic, but keeping threat actors at bay will take more than researching AI-enabled tools and defenses. The war is ongoing, and companies that haven’t already outfitted themselves with these defenses must make it their top priority.

3) Architect Zero-Knowledge Identity Verification with Homomorphic Authentication

The traditional model of centralized identity stores often creates points of failure that become increasingly vulnerable as AI-powered attacks grow more sophisticated. Thankfully, that traditional model is no longer the only option available. AI-ready identity security programs represent a significant paradigm shift from password-based or even biometric-based authentication to proof-based authentication, which requires users to demonstrate knowledge or possession of specific cryptographic secrets that change with each interaction, all without exposing underlying identity data to any single system or actor.

Implementing homomorphic encryption schemes allows AI systems to perform authentication and authorization operations on encrypted identity data without decrypting it. More than ever, corporations must embrace this shift, deploy verifiable credential systems based on zero-knowledge proofs, and empower their AI systems to make access decisions based on cryptographically verified claims without accessing raw identity information. That last bit is crucial, as it reinforces the company’s data security efforts and reassures its human employees that their information remains secure from threat actors.

4) Establish Autonomous Identity Governance with Self-Healing Access Controls

AI readiness is no longer a “nice-to-have” feature for modern companies; it’s an essential pillar for success. Unfortunately, as you can probably expect, current identity governance toolsets are struggling to keep up, inadvertently providing AI-powered attackers with a vulnerability they can exploit. To combat this, organizations can implement autonomous governance systems that optimize access controls without human intervention, treating access control as a continuous optimization problem rather than a periodic administrative task.

The autonomy imperative demands deploying reinforcement learning systems that automatically adjust permissions based on real-time risk assessments, business context, and behavioral patterns while maintaining least-privilege principles. This requires implementing multi-agent AI systems where different agents specialize in various aspects of identity governance. With a team of these AI agents, companies can rest easier knowing they’re maintaining an optimal security posture while also developing self-healing capabilities that automatically respond to identity-based attacks without disrupting legitimate business operations.

5) Create Adaptive Context-Aware Authentication with Ambient Intelligence

Static authentication requirements cannot address the dynamic risk profiles of modern work environments, especially with threat levels that fluctuate based on countless contextual variables that human analysts cannot process at scale. AI-ready identity security demands adaptive authentication systems capable of continuously adjusting security requirements based on contextual intelligence gathered from the entire digital and physical environment.

To stay relevant in this “context revolution,” teams should deploy ambient intelligence networks that gather contextual signals from IoT devices, network sensors, application logs, and environmental data, creating comprehensive risk profiles informing real-time authentication decisions. For example, advanced sensor fusion allows AI systems to correlate seemingly unrelated environmental factors to detect sophisticated attacks, unusual building access patterns, network anomalies, or user behavior changes that can indicate coordinated insider threats.