Partner POV | Is Your AI Security Strategy Falling Short? Inside ExtraHop's 2026 Global Threat Landscape Report
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This article was written and contributed by, ExtraHop.
The cybercriminal playbook has officially evolved, and traditional tripwires aren't catching them.
The 2026 ExtraHop Global Threat Landscape Report documents a troubling reality: despite heavy investments in AI security, the detection gap is widening.
From autonomous agents creating new attack surfaces to attackers mimicking legitimate workflows, defenders are fighting an uphill battle.
Here is a first look at the biggest risks facing security operations right now, why attackers are staying hidden for longer, and how to evolve your defense for the AI era.
The AI Threat Landscape
What are the biggest AI cybersecurity risks in 2026?
The modern enterprise perimeter has shifted, rendering legacy defense architectures obsolete.
According to the report, AI agents and generative AI applications rank as a top attack surface concern globally. Specifically, 55% of respondents identified these as a top risk compared to public cloud (44.7%), third-party integrations (35.2%), and identity infrastructure (33.8%).
This expanded perimeter directly translates into vulnerability, with attackers actively weaponizing emerging AI infrastructure to breach corporate networks.
When asked to identify the primary source of security incidents, data exposures, or near-misses over the last year, organizations reported the following AI-driven root causes:
- 40%: AI-enhanced external attacks
- 38%: Compromised AI identity or session theft
- 36%: Third-party or supply chain breaches involving integrated AI systems
- 35%: Employees utilizing unvetted public AI tools, inputting proprietary data or credentials
- 31%: Autonomous agents executing unintended network actions or hallucinating system-level commands
The Consequences of a New Cyber Frontier
How long can cybercriminals stay hidden inside an enterprise network?
The 2026 GTLR data makes the answer clear: too long.
The longer an attacker goes undetected, the deeper they embed themselves. And right now, the data shows that threat actors are gaining serious ground.
When asked at what point they recognized their organization was being targeted by a ransomware attack, nearly half (49%) of organizations did not identify the threat until data exfiltration was already underway or later.
Even more alarming, about 15% didn't recognize the attack until a ransom demand arrived.
When asked how long the threat actor had access to their systems following their most recent ransomware incident, organizations reported a mean of 2.4 weeks, up from 2 weeks the prior year.
This widening detection gap – the critical window between a compromise and its discovery – means most organizations are completely blind until it's far too late.
To pull this off, threat actors have traded loud, obvious attacks for stealth tactics designed to bypass traditional tripwires.
- In 41% of cases, attackers used encrypted channels to bypass detection entirely.
- 38% of attacker activity mirrored legitimate, authorized workflows and processes.
- 34% of incidents involved valid high-privilege account permissions.
The Reality of AI in Security Operations
Is AI in cybersecurity mostly hype?
Despite the AI boom, security teams are still heavily bogged down by human-dependent processes. On average, a 45% level of manual intervention is required across the stages of the threat lifecycle (detection, triage, investigation, and response).
Similarly, analysts spend less than half their time (44%) on proactive efforts like threat hunting and detection engineering, caught in a constant loop of reactive activity
Even when teams do adopt AI, it's adding to the noise, draining finite analyst resources instead of optimizing them. Instead of streamlining operations, the 2026 ExtraHop Global Threat Landscape found AI-generated alerts led to false positives that negatively impacted investigations nearly 30% of the time.
Next Steps for Adaptive Defense
How do we evolve our cybersecurity strategy for AI?
Evolving your cybersecurity strategy for the AI era boils down to two simple rules: You have to see it all, and you have to do it in real-time. If you have blind spots, attackers will exploit them. If your response times are measured in hours or days instead of seconds, attackers will outpace you.
To bridge the detection gap, security teams need comprehensive network visibility and response times measured in seconds, not hours.
3 Steps to Take Today
- Map and Monitor Your Attack Surface. Treat every AI tool as a potential entry point. Shadow AI, autonomous agents, and third-party integrations create unique network traffic that traditional asset inventories completely miss.
Conduct a comprehensive audit of every generative app, autonomous agent, and vendor integration currently running in your environment, then implement continuous monitoring to track exactly what data these systems are accessing and where they are sending it. - Build a Baseline of Normal Behavior to Automate Anomaly Alerts. Static signatures are useless against AI-mutated threats. When attackers are mimicking authorized workflows to stay hidden, you need to know what "normal" actually looks like to spot the fake.
Document baseline patterns for timing, data volume, and access rights across all users, systems, and workflows, then configure automated alerts for deviations, such as off-hours access, massive data transfers, or processes running outside their typical scope.
- Eliminate the Blind Spots. Attackers rely heavily on encrypted channels and hijacked high-privilege credentials because they look legitimate. You have to close this gap to catch them before they move, encrypt, or exfiltrate.
Deploy passive, line-rate decryption to analyze encrypted payloads in real time without interrupting or modifying your standard network traffic. To address credential theft, establish real-time monitoring over privileged accounts to instantly flag unusual credential movement (tracking the where, when, and how of administrative access).