Identity Security vs AI-Driven Threats

August 29th, 2025 - Written By Cyber Labs Services

Securing the Future How Identity Security Protects Against AI-Driven Threats

Introduction

A succession of high-profile breaches worldwide has shown that even advanced economies remain vulnerable to modern cyber threats. As artificial intelligence (AI) becomes increasingly embedded in business operations, the stakes are rising.

Organizations are adopting AI to improve productivity, customer experience, and competitiveness. Yet with these benefits comes an often-overlooked cost: new and amplified risks.

CyberArk’s research highlights AI as a “triple threat” to cybersecurity. It is:

Being exploited by attackers as a powerful offensive weapon,

Used defensively as a security enabler, but with its own blind spots,

Creating entirely new identity and access challenges, especially with machine accounts and shadow AI.

To build resilience in this evolving landscape, businesses must place identity security at the heart of their AI strategies.

AI-Powered Attacks: Same Threats, New Problems

AI has taken traditional cyberattacks to the next level.

Phishing, the #1 entry point for breaches, has evolved from clumsy, misspelled emails into sophisticated scams using deepfakes, cloned voices, and hyper-personalized messages.

Attackers can now generate malware, crack passwords, and mimic trusted insiders in seconds.

Reports show that nearly 70% of UK organizations fell victim to phishing last year, with over a third experiencing multiple incidents. Globally, phishing attacks using AI-generated voice and video impersonation are on the rise, tricking even experienced employees.

Why this matters: Perimeter defenses alone are not enough. If an attacker can convincingly impersonate a CEO, supplier, or colleague, identity becomes the last line of defense.

Mitigation: Strong multi-factor authentication (MFA), adaptive identity verification, and employee training that emphasizes “trust but verify.”

AI in Defense: A Double-Edged Sword

AI isn’t just helping attackers—it’s also revolutionizing defense.

Security operations centers (SOCs) now use AI and large language models (LLMs) to detect anomalies, spot early signs of breaches, and automate repetitive tasks.

Nearly nine in ten organizations use AI for monitoring and detection, with half predicting AI will drive the biggest portion of cybersecurity spending in the next year.

This is a positive shift: AI acts as a force multiplier, helping small security teams manage a massive workload. But there’s a catch:

Over-reliance on AI can create false confidence. Models trained on poor-quality data may miss critical threats.

AI tools can inherit bias or develop “blind spots,” creating opportunities for attackers to slip through unnoticed.

Without human oversight, security teams’ risk assuming the AI “has it covered”—a dangerous assumption in high-stakes environments.

Mitigation: Treat AI as an enabler, not a replacement. Human expertise, rigorous model testing, and continuous oversight are crucial.

Expanding Attack Surfaces: Machine Identities & Shadow AI

The third, and perhaps most overlooked, part of the triple threat is the explosion of machine identities and shadow AI:

In many enterprises, machine identities now outnumber human identities by 100 to 1.

These AI agents, bots, and service accounts often hold elevated privileges but lack governance. Weak credentials, shared keys, and poor lifecycle management make them easy targets.

At the same time, employees increasingly use unauthorized AI tools (“shadow AI”) to speed up tasks—copying sensitive data into chatbots or generators without security controls.

The risk? Data leaks, regulatory breaches, and reputational damage. In some cases, confidential data used in shadow AI has been absorbed into public models—exposing sensitive corporate information to anyone.

Mitigation:

Apply least privilege and just-in-time access to machine accounts.

Monitor privilege escalation across AI agents.
Provide secure, approved AI tools so employees don’t feel forced to “go rogue.”

Why Identity Security Is the Answer ?

In this environment, identity is the new perimeter. To mitigate the AI triple threat, organizations must build security around who (or what) is accessing systems and data.

Key steps include:

Real-time visibility: Monitor all identities—human, machine, and AI agents.

Adaptive authentication: Go beyond static MFA with context-aware checks (location, device, behavior).

Continuous monitoring: Use Identity Threat Detection and Response (ITDR) to flag suspicious behavior early.

Governance and culture: Educate staff on AI risks, set clear policies, and foster a “report without hesitation” culture.

Forward-looking companies are already adapting their frameworks to treat AI agents like human employees—with onboarding, monitoring, and offboarding processes.

AI adoption is accelerating, and so are the risks. The **AI Triple Threat—offensive use of AI, defensive blind spots, and identity sprawl through machine accounts—**represents a new frontier in cybersecurity.

But businesses don’t need to slow down their innovation. By embedding identity security into every layer of digital strategy, organizations can safely harness AI while minimizing exposure.

At a time when both attackers and defenders are empowered by AI, one truth stands above all: Securing AI begins and ends with securing identity.