Identity-based intrusions now dominate cybersecurity threats
Most cyberattacks today don’t rely on malware. The majority of breaches happen because attackers simply log in, using real, stolen credentials. That means they’re not breaking through walls; they’re walking through open doors.
This shift puts identity at the center of enterprise security. CrowdStrike’s 2025 Global Threat Report shows 79% of attacks are now malware-free, and 90% of organizations experienced identity-related intrusions over the past year. Of those, 80% say the damage could have been limited with better identity tools in place. It’s clear: identity has become the primary vector for breaches.
Security professionals often focus heavily on perimeter defenses, things like firewalls and antivirus software. But attackers don’t have to break that perimeter anymore if they already have valid credentials. This makes traditional security postures outdated. What’s needed is continuous identity monitoring, systems that can detect behavior anomalies in real time and act immediately.
Securing identity is no longer just an IT problem. It’s an operational priority that impacts customer trust, data integrity, and business continuity. You don’t need an army of analysts; you need intelligent tools that recognize suspicious behavior the moment it occurs.
Cristian Rodriguez, Field CTO for the Americas at CrowdStrike, put it clearly: “Attackers don’t break in anymore, they log in.” He’s right. That’s why security needs to evolve from guarding buildings to guarding identities. Treat every login like a potentially harmful event until proven otherwise. Proactive identity protection isn’t an option; it’s now a business-critical requirement.
Generative AI is accelerating both the sophistication of attacks and the capabilities of defenses in identity security
Generative AI has changed the game, on both offense and defense. Attackers now use AI to create highly convincing scams. Voice phishing (vishing) and deepfake attacks are growing fast. Between the first and second halves of 2024, vishing attacks increased 442%. These aren’t amateur scams; they’re automated, scalable, and hard to detect.
That’s one side of the coin. The other side is more promising.
Defenders now have access to generative AI tools that adapt and respond instantly. These platforms create behavioral baselines, understanding what normal user activity looks like, so they can spot deviations in real time. If an account suddenly behaves outside of its pattern, access can be blocked, privileges revoked, or authentication stepped up.
This is what’s happening at scale. At Cushman & Wakefield, a global real estate firm with 50,000 employees, traditional security systems just couldn’t keep up. Too many remote users, too much activity, too much complexity. Things didn’t scale. Eric Hart, their Global CISO, said, “We needed real-time identity protection that could seamlessly integrate into our broader security strategy.” They switched to CrowdStrike’s Falcon Next-Gen Identity Security, an AI-powered platform that watches over every user, machine, application, and service account.
These platforms don’t just detect threats faster. They stop attackers from moving laterally, from jumping between systems, when credentials are compromised. That time gap is critical.
Cristian Rodriguez from CrowdStrike emphasized this: “With generative AI, defenders finally have tools that can learn, adapt, and respond in real time.” This is what’s changing, machine-speed attacks now require machine-speed defenses.
In this tug of war between attackers and defenders, those who embrace AI will move faster. It comes down to one rule: adapt or get left behind. AI isn’t a future consideration; it’s already redefining the threat landscape. Use it.
Machine identities vastly outnumber human identities, demanding security systems that operate at machine speed
Today’s enterprises are driven by automation, cloud services, and distributed applications. That environment has produced a massive growth in machine identities, everything from service accounts and APIs to microservices and bots acting autonomously across infrastructures. The scale is no longer even close. On average, machine identities outnumber human users by 45 to 1. That’s not a small margin, it’s a seismic gap.
The problem is that most legacy identity security tools were never designed to manage that volume or speed. They rely on static rules and periodic reviews, quarterly or longer, to spot misuse or excessive permissions. That cycle is too slow. Threat actors can breach one identity and move laterally through a network in just 51 seconds. Those numbers aren’t theoretical. That’s what CrowdStrike’s real-time telemetry has measured happening in production environments.
Organizations that haven’t modernized are exposed. The more machine identities exist without strong management, the easier it is for attackers to compromise infrastructure and stay undetected longer. Static access policies, manually configured privilege boundaries, and delayed renormalization allow threats to spread before any alarm is triggered.
If your security architecture isn’t operating at machine speed, you’re going to have blind spots. These aren’t just operational inefficiencies, they’re exploitable weaknesses. Real-time data processing, continuous identity validation, and automated access adjustments are no longer edge capabilities. They have to be core parts of your identity fabric.
Executives need to think beyond traditional governance. Machine identities require the same visibility and adaptation as user identities. Smart systems must treat them as first-class security citizens, able to be monitored, audited, and, critically, shut down or re-permissioned instantly when the behavior changes. This is the only viable strategy against attacks that are equally fast, automated, and persistent.
Legacy identity and access management (IAM) systems and vulnerability assessment models are no longer effective
Traditional IAM systems were built for stability, not for adaptability. They enforce access rules based on fixed models, group permissions, role assignments, scheduled reviews. That structure worked in simpler environments. It doesn’t hold up now.
Threats today adapt in real time. They exploit any delay, and that’s exactly why static access models and outdated vulnerability scoring systems like CVSS are falling short. The numbers back this up. According to Ivanti, 73% of actively exploited vulnerabilities were classified as only “Important” under traditional systems, not “Critical.” That means security teams, following standards, deprioritized major threats without realizing it.
Legacy processes assume you can react tomorrow or next week. The current threat environment makes that assumption dangerous. Prioritization needs to be driven by live threat intelligence, not historical scoring. Enterprise systems should be assessing vulnerability severity not only by technical risk but by real-world exploit data, potential impact, and the criticality of affected assets.
AI-driven platforms resolve this by dynamically scoring vulnerabilities using real-time inputs, whether an exploit is in the wild, how likely it is to be targeted, and how important the asset is in your architecture. Ivanti’s AI solution, for example, enables security teams to patch critical vulnerabilities 85% faster than legacy workflows.
For leaders responsible for security budgets and operational resilience, this difference translates into real value. You reduce risk exposure faster, respond quicker, and avoid costly remediation efforts after damage is done.
Mike Riemer, Field CISO at Ivanti, said it directly: “Traditional CVSS scores are nearly worthless for prioritization.” He’s not overstating it. Without live intelligence and adaptive scoring, your threat prioritization tools are too slow, and in some cases, actively misleading.
You can’t rely on yesterday’s assumptions to manage today’s risks. Intelligent, AI-enhanced IAM and vulnerability systems identify the threats that matter most and eliminate delays in response. That’s good security, and it’s efficient security.
Large language models (LLMs) are revolutionizing identity governance by embedding identity directly into AI reasoning
Large Language Models are now doing more than assisting with text generation. They’re actively reshaping how organizations govern digital identities. Traditional identity governance systems treat permissions and access logic as external configurations, something you check against. LLMs take a different approach. They can embed identity context directly into their decision-making processes. That makes identity not just a policy layer, but part of real-time AI reasoning.
This matters because organizations increasingly rely on AI systems to make live decisions, often involving sensitive workflows or data access. In these scenarios, identity verification needs to be deeply integrated. The AI should know who it’s acting on behalf of, what that user is permitted to do, and whether their behavior deviates from expected patterns.
Reputation’s Vice President of AI, Carter Rees, explains the shift well: “We are moving toward an identity-embedding framework where role-based permissions and behavioral baselines are encoded directly into model reasoning, not just enforced in admin dashboards.” He also warned of the risk: “User embeddings are sensitive identity artifacts… They must be encrypted, monitored, and governed under HIPAA and GDPR.”
Security leaders need to treat these user embeddings like credentials. They can expose personal or protected data if improperly handled. Embedding inversion attacks, where private details are reconstructed from model inputs, have been demonstrated in research. So, as identity shifts closer to the logic layer of AI, the guardrails need to be stronger and aligned with privacy regulations by default.
Research from Google’s USER-LLM project backs these developments. By using cross-attention on user embeddings during AI inference, accuracy improves, and outcomes are better linked to real user context. But this also validates the need for stronger security models and clearer identity boundaries embedded in large-scale AI deployments.
Embedding identity into AI doesn’t just improve trust, it changes how AI-enforced decisions can be audited and controlled. For executives deploying AI across regulated sectors like healthcare, finance, or critical infrastructure, this is where value and compliance intersect.
Real-world deployments of gen AI-driven identity platforms yield significant operational benefits
Generative AI isn’t speculative anymore. It’s being deployed across global enterprises, and the results are consistent, better speed, fewer errors, and stronger security outcomes. For CISOs and executive teams evaluating ROI, this is no longer about potential. It’s operational proof.
Enterprise platforms using gen AI for identity protection are achieving outcomes that manually driven systems simply can’t match. At Land O’Lakes, incident investigation time dropped from eight hours to 38 minutes, a 92% improvement. This means security teams aren’t chasing minor alerts for hours. They’re focusing on verified threats and resolving them fast.
These platforms establish behavioral baselines for every identity, user, machine, or AI agent, and then monitor for deviations. That includes service accounts that usually access ten resources suddenly touching hundreds. These systems act instantly: enforcing stronger authentication, updating privilege groups, or shutting down sessions if needed. All without human intervention.
Excessive privileges, a problem in most environments, have also been dramatically reduced. Capital One and Fidelity Investments used AI-driven tools from SailPoint and ForgeRock to cut unnecessary permissions by up to 95% within six months. That not only tightens access but also shrinks the overall attack surface. You reduce the number of exploitable paths before attackers even try.
And when breaches do happen, these systems minimize damage. IBM’s 2024 Cost of a Data Breach Report shows organizations using AI-driven security automation cut their incident lifecycle by 108 days. They saved on average $2.22 million per breach.
Security teams also waste less time chasing false alarms. AI-enabled platforms like CrowdStrike Falcon and Cisco SecureX reduce false positives by more than 90%, helping analysts focus only on real, high-confidence alerts. For leadership, this means lower operational workload, faster ROI, and better alignment between cyber resilience and business continuity.
Executives need to focus on investments that improve real metrics. Time saved. Threats blocked. Costs reduced. Gen AI is delivering all three, at scale.
The vendor ecosystem is evolving rapidly, delivering AI-powered identity tools with demonstrable ROI
The security vendor landscape is moving fast. The ones that matter are not just adding AI, they’re building with it at the core. For C-suite leaders, this is about more than keeping up. It’s about deploying tools that deliver measurable results right now.
CrowdStrike’s Charlotte AI has reduced analyst workloads by embedding conversational threat hunting directly into endpoint and identity telemetry. This isn’t a minor feature upgrade, it’s a structural shift that allows one analyst to handle what used to require teams. Ivanti’s Neurons platform applies AI to automate patching using smart deployment logic. Updates are no longer manually managed, they’re prioritized and executed based on live risk, reducing downtime.
Microsoft’s Security Copilot integrates AI across Azure, Sentinel, and Defender, giving enterprises an end-to-end system for real-time threat forecasting and automated response. Okta’s Adaptive MFA doesn’t just rely on credentials, it uses behavioral profiling to stop attacks even if authentication is compromised. ForgeRock’s Autonomous Identity eliminates stale access through continuous permission recalibration, helping organizations maintain least-privilege baselines with less manual effort.
Vendors offering capabilities that unify identity, context, and telemetry into one framework are gaining traction. SailPoint’s IdentityAI automates privilege hygiene. SentinelOne integrates AI-driven identity threat detection across endpoints. CyberArk uses generative AI in its PAM suite to minimize privilege creep. Microsoft Entra ID is now embedding AI to support adaptive access control with dynamic identity risk scoring.
For security executives, this is not just about feature sets, it’s about choosing platforms that convert capability into results. A Forrester Total Economic Impact study reports a 310% average ROI from AI-powered identity solutions, with full payback achieved within six months. Add to that the 90%+ reduction in false positives seen with platforms like CrowdStrike Falcon and Cisco SecureX, and the operational leverage is clear.
Choosing the right vendors now isn’t about future planning, it’s about performance today. Go with those who are executing aggressively and turning AI into outcomes.
Investment in identity security is rapidly increasing due to accelerating AI-driven threats and evolving defense mechanisms
The market signals are clear, security spending is scaling up fast, and identity is at the core of it. Enterprises are responding to the rise of generative AI-powered threats with long-term investment in adaptive defense systems. This trend isn’t short-term or reactionary. It’s being driven by necessity and future-proofing strategies.
Gartner forecasts global information security spending to reach $213 billion in 2025. And it doesn’t stop there. They project $323 billion by 2029 as companies prioritize ongoing protection and capabilities that evolve with the threat landscape. Identity and Access Management (IAM) alone is set to double in size. IDC reports the IAM market will grow from $23.5 billion in 2024 to $47.1 billion in 2028.
This is where modern architectures are headed, away from siloed authentication and static privilege maps, and toward systems that constantly learn, adapt, and respond using real-time intelligence. The increased volume of machine identities, user accounts, and third-party integrations make this shift critical to avoiding systemic vulnerabilities.
Enterprises understand that outdated IAM infrastructure creates lag, both in detection and response. That lag means business risk. AI doesn’t just bring speed, it brings adaptability. That’s the advantage security teams need when dealing with evolving threat behaviors and the sheer scale of connected systems.
Security investments should no longer be viewed as insurance. They enable resilience, operational continuity, and customer trust. For executives, a modern identity security architecture anchors that capability across the business. The budget growth reflects this strategic shift, identity security is now a core pillar of enterprise defense, and it’s being funded accordingly.
Recap
Most breaches today don’t start with code, they start with credentials. That shift isn’t coming. It’s already here. And it’s not just part of the threat landscape, it is the threat landscape.
Executives shouldn’t treat identity security as a technical concern buried in IT. It’s operational, financial, and reputational. If someone logs into your systems using real credentials and nobody notices for hours, or days, you’re not just exposed. You’re compromised in silence.
AI changes the equation. Generative models, behavioral engines, and real-time telemetry aren’t future capabilities. They’re functioning now in high-scale environments, and they’re already outperforming human workflows. They detect fast, act faster, and prevent attackers from moving through your infrastructure unnoticed.
The most resilient organizations have already made the shift, replacing rule-based systems with platforms that learn, adapt, and intervene in real time. Those that haven’t, fall behind with every missed alert, every false positive, every walk-in access that should’ve been stopped.
You don’t need more tools. You need the right platform, one that unifies identity, learns continuously, and defends at machine speed.
If you’re investing in security, start with identity. Every compromise starts somewhere. Increasingly, it starts with a login. That’s where your defense needs to begin.
