Modern breaches increasingly begin with trusted access rather than malware or software exploits. By abusing credentials and permissions granted to legitimate identities, attackers gain initial access to systems, data, and business processes across the enterprise.

At Abnormal Innovate, we explored how this shift has reshaped the account takeover threat landscape. Identity abuse has become the most reliable way for attackers to gain control because it operates inside systems designed for trust.

Incident response data shows that identity compromise is now central to many real-world breaches. More than 60 percent of incident response engagements involve identity-based access, and nearly 70 percent of ransomware intrusions begin with valid accounts rather than exploited vulnerabilities.

This trend reflects how identity systems function. Authentication, sessions, and cloud access are optimized for user experience and interoperability. Once an attacker gains access through a legitimate identity, that trust extends across email, SaaS platforms, and internal workflows with very little friction. Using identity as the entry point has become more reliable than exploiting systems directly.

Artificial intelligence did not create identity attacks, but it has significantly reduced the effort required to execute them at scale.

Reconnaissance that once required manual research can now be generated in seconds. Social engineering messages can be tailored automatically to specific roles or workflows. OAuth applications can be branded convincingly with minimal effort. Even post-authentication activity can be shaped to resemble legitimate user behavior.

This matters because identity attacks depend on context rather than technical flaws. AI excels at producing realistic language, matching tone and timing, and adapting quickly when an attack method fails. As a result, identity abuse has become faster to launch, easier to repeat, and harder to distinguish from everyday activity.

Modern account takeover succeeds because it operates entirely inside trusted workflows.

In OAuth-based attacks, users are shown legitimate permission screens rendered by their identity provider. When access is approved, attackers receive valid tokens without stealing credentials or triggering multi-factor authentication challenges. From the system’s perspective, everything appears normal.

Once inside, attackers move deliberately to build situational awareness. They read email threads, review CRM records, and observe vendor and finance workflows. Research shows that nearly 80 percent of modern intrusions are malware-free, allowing attackers to remain hidden while they build context. This gradual misuse of access, rather than a single malicious action, defines modern account takeover. Attackers blend into normal operations before taking action.

Most traditional security controls focus on a narrow slice of the attack surface:

• Rule-based email security tools look for malicious links, attachments, and payloads.

• Identity providers evaluate sign-in risk signals and authentication anomalies.

• Rules and signatures rely on predefined logic that must exist before an attack occurs.

• Threat intelligence reacts after indicators are observed in other environments.

Together, these approaches leave a gap when attackers operate inside trusted access paths and avoid obvious artifacts.

Identity abuse breaks these assumptions. Attackers rely on valid credentials, approved access, and behavior that appears legitimate. No single event looks malicious on its own. What matters is the sequence of actions over time.

This is why rules-based detection struggles against modern account takeover. The signal is not a known indicator, but a pattern of behavioral drift.

Defending against modern account takeover requires correlating behavior across identity, email, sessions, and SaaS access. Rather than treating each anomaly as a separate alert, behavioral systems combine weak signals into a coherent picture of compromise.

Abnormal applies this approach to account takeover detection. The platform learns how identities normally behave across communication patterns, access sequences, and workflows. When behavior deviates from that baseline, those deviations are correlated into high-confidence account takeover cases, even if no individual signal would trigger a static rule.

This is the same principle behind Abnormal’s behavior-based email security. Instead of relying on predefined logic, the system learns what legitimate behavior looks like and identifies when context changes in a meaningful way.

Abnormal’s Account Takeover Protection uses behavioral AI, automated threat hunting, and shared intelligence across environments to detect identity abuse early.

Detection improves continuously as models adapt to new patterns rather than relying on manual rule updates. Automated Threat Hunter correlates activity across time and accounts, allowing coordinated campaigns to be uncovered even when early signals appear individually benign.

When compromise is confirmed, Abnormal can automatically terminate sessions, disable accounts, and initiate recovery actions. This reduces attacker dwell time and helps security teams contain identity-based attacks before they reach critical business workflows.

Identity has become the primary pathway into modern attack chains, and AI has accelerated this shift. Effective account takeover defense now depends on understanding behavior after authentication, correlating subtle signals over time, and responding before attackers can operationalize access.

These ideas were explored in depth during The Modern Account Takeover Threat session at Abnormal Innovate.

Watch the full presentation to see how modern account takeover unfolds.