DTEX today introduced its expanded AI Risk Management product, extending its platform to secure enterprise use of generative AI tools and autonomous AI agents. As GenAI applications, copilots, and AI agents increasingly operate with access to enterprise data, systems, and workflows, most security solutions still lack the ability to determine human or AI agent intent. DTEX closes that gap with AI Risk Management: a comprehensive suite of AI-native capabilities that apply behavioral intelligence to detect and deter both human and AI-driven risk with the speed and precision of AI.
By combining AI risk management with autonomous investigation and response, DTEX enables organizations to accelerate AI adoption with the visibility, control, and operational confidence required to safely scale AI-driven productivity and innovation across the enterprise.
Monitor and Protect AI Activity
As AI agents begin operating autonomously across enterprise systems, organizations face a new category of risk. Unlike traditional software, AI agents can interpret instructions, access sensitive data, interact with external systems, and make decisions with limited human oversight. Securing these environments requires more than activity monitoring. It requires understanding what the agent was instructed to do, how behavior evolves over time, and whether actions align with expected intent.
DTEX delivers comprehensive visibility into how AI is used across the enterprise and applies deep behavioral context to identify emerging risk before it becomes a breach.
With AI Risk Management organizations can:
- Discover sanctioned and unsanctioned AI usage across users, endpoints, and workflows, including browser, IDE, application, and embedded AI activity.
- Identify shadow AI and embedded copilots in real time, dynamically building sanctioned tool inventories and automatically classifying the risk of unknown or unmanaged AI tools.
- Monitor prompts, responses, and data movement at a granular level, including uploads, downloads, and AI-generated content, to detect leakage of source code, intellectual property, and sensitive enterprise data.
- Classify prompts and interactions to support auditing, compliance, and threat investigations, enabling security teams to understand not just what was asked, but why, through behavioral context and intent analysis.
- Analyze AI activity to infer both human and AI agent intent, distinguishing normal experimentation from risky or malicious behavior by correlating prompts, historical patterns, behavioral baselines, and agent actions over time.
- Differentiate human versus AI-driven actions and deliver deep visibility into “Computer Use” AI (CUI), including what an agent was instructed to do, how it executed tasks, and the detailed lineage of actions performed across enterprise systems.
- Detect and prevent autonomous agent-driven data exfiltration using behavioral monitoring, prompt lineage, and AI risk models that proactively identify high-risk agentic behavior and the intersection between human and AI risk.
In one early deployment, DTEX identified an autonomous AI agent exposing sensitive enterprise data despite operating within its intended workflow and permissions. By correlating prompt lineage, behavioral patterns, and contextual activity over time, DTEX surfaced the risk before it resulted in a security incident.
Act on Risk with Autonomous Security Agents
To make AI Risk Management operational, DTEX is also introducing autonomous security agents that apply behavioral context and risk modeling to automate investigation and threat analysis. This enables organizations to differentiate human vs AI-driven activity, track behavioral patterns over time, and understand how AI systems interact with data and identities.
Triage Guardian Agent
Built on more than 20 years of DTEX i³ behavioral expertise, Triage Guardian applies a multi-agent approach to deliver consistent, defensible triage outcomes at scale. Unlike traditional alert-driven workflows that evaluate isolated events, Triage Guardian continuously analyzes behavioral context before, during, and after an incident, allowing agents to effectively rewind and fast-forward investigative timelines to understand how risk evolved over time. It automates investigation workflows, gathers contextual evidence, and applies structured human oversight through independent reviewer agents that validate findings, minimize bias, and ensure conclusions remain evidence-backed. By combining behavioral intelligence with analyst-grade decision logic, Triage Guardian dramatically reduces false positives while minimizing missed risks that conventional triage approaches often fail to detect.
Threat Hunter Agent
Threat Hunter enables proactive threat discovery through agentic workflows, continuously assessing the evolving risk landscape, generating detailed threat analysis, and identifying previously unknown threats before they surface in an incident. Analysts can initiate complex threat hunts using natural language, allowing Threat Hunter to determine how to execute the investigation, correlate findings, and surface relevant risk autonomously. Built on more than 25 years of DTEX i³ threat hunting expertise, including collaborative research with MITRE and FVEY defense partners, Threat Hunter applies proven analyst tradecraft and investigative context to every hunt at machine speed.
Availability
DTEX AI Risk Management is currently available in private preview. Organizations can request access, with broader availability expected next quarter.
Organizations can learn more and request access at www.dtex.ai/ai-risk.
New Research Reveals the More Confident Organizations Are in Their AI Security, the More Likely They’ve Already Been Breached
Posted in Commentary with tags FusionAuth on June 9, 2026 by itnerdFusionAuth today released its 2026 State of AI and Identity Report, detailing how AI is reshaping identity infrastructure, security posture, and enterprise trust. The findings reveal a profound and counterintuitive crisis: the organizations that feel most prepared are getting hit the hardest.
Sixty-five percent of respondents reported a confirmed AI identity-related security incident in the past 12 months, with another 23% reporting a near miss. Only 12% emerged from the past year without an incident or close call. But the headline finding is not the breach rate alone; it is who is getting breached.
Among organizations that rated themselves “extremely confident” in their AI security posture, 84% had already experienced a confirmed incident. That figure drops to 64% among those “very confident,” and to just 17% among those who are “not so confident.” The gradient is near-perfect: confidence and breach rates move together.
Key Findings at a Glance
Confidence is Tracking the Wrong Thing
The report’s most striking finding has significant implications for how the industry benchmarks AI security readiness. Organizations at the top of the confidence scale share a common profile: they are deploying AI broadly, have comprehensive policies, have formalized lifecycle processes, and are investing heavily. They are doing everything a mature organization should, yet they are still being breached at high rates.
The report also notes that organizations with more mature security programs are better at detecting incidents, meaning lower-confidence organizations may not be safer, but simply have less visibility into what is already happening.
Architecture is the New First-Order Security Variable
The deployment model an organization uses for its identity platform correlates strongly with breach outcomes. Organizations using multi-tenant SaaS identity platforms report confirmed incidents at more than twice the rate of those using self-hosted or on-premises deployments: 83% versus 38%.
In a shared SaaS environment, a single compromised token or misconfigured policy does not stay contained. It cascades across every AI workflow connected to the identity layer, model access, data pipelines, automation actions, and downstream services, creating a fundamentally different blast radius than a self-hosted or isolated deployment.
The highest-risk profile in the study is not a low-maturity organization. It is the opposite: companies running AI in production, using AI broadly across the workforce, and operating on multi-tenant SaaS identity infrastructure. In this cohort, 90% reported a confirmed incident and 96% faced shadow AI challenges.
Identity is Now a Commercial Trust Problem
AI identity risk has moved beyond the security team. Eighty-five percent of respondents have faced customer, partner, or regulatory demands to demonstrate tenant isolation at least occasionally, while 56% face it frequently. Tenant isolation has shifted from a backend implementation detail to a commercial requirement that now determines whether enterprise deals close.
Among organizations where AI is the primary driver of identity reevaluation and customers frequently demand proof of isolation, 99% reported a confirmed incident, and 95% are planning significant increases in investment, pointing to a buying motion driven by urgency rather than planning.
Investment is Moving from Incremental to Structural
Ninety-three percent of respondents say AI is already causing or contributing to a reevaluation of identity infrastructure. Sixty-six percent are planning a significant increase in investment, and 91% expect some level of increase in the next 12–18 months. The top evaluation criteria reflect an architectural shift: machine identity at scale (72%), deployment flexibility (57%), fine-grained authorization (54%), and tenant isolation (32%). Total cost of ownership ranked last at 11%.
About the Research
The 2026 State of AI and Identity Report is based on a survey of 312 technology and security leaders, screened for relevance to AI, identity, and security decision-making. Respondents include CTOs, CISOs, VPs and Directors of Product, Engineering, Security, and Platform/Infrastructure across a range of company sizes and industries. The survey was conducted by FusionAuth in early 2026.
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