Enterprise AI adoption is accelerating faster than most security and governance models can keep up with.
Ahead of the upcoming Zscaler ThreatLabz 2026 AI Security Report, Help AG, in partnership with Zscaler, is sharing early insights emerging from global ThreatLabz research – combined with Help AG’s regional and operational perspective for enterprises operating in regulated and compliance-driven environments.
In this blog, you’ll gain early insight into how global AI usage trends are already translating into real-world risk, governance, and control challenges, and how these signals can help you prepare for the deeper findings to come as organizations move toward 2026.
AI Is Now Embedded, Not Optional
By the end of 2025, AI is no longer confined to pilot initiatives or isolated tools. AI capabilities are now embedded directly into everyday enterprise workflows, often operating behind the scenes as part of existing systems and services.
In many cases, users – and even security teams – may not be explicitly aware when AI models are involved. AI-driven interactions are automated, high-frequency, and deeply integrated into how work gets done.
This shift fundamentally changes the security landscape. Organizations increasingly struggle to answer basic but critical questions:
- Where is AI being used?
- Which data is being shared?
- Which models are involved?
Without visibility into these fundamentals, governance and risk management fall behind real-world usage.
What has changed most is not AI adoption, but the assumptions around control. Many enterprises still treat AI as an explicit user choice, while in reality it already operates automatically within everyday workflows, executing decisions and transformations in the background. This creates a growing gap between policy and actual usage.
To address this, Help AG works with enterprises to assess where AI is embedded, identify gaps in existing governance and compliance models, and help to define clear ownership and guardrails aligned with regulatory and operational requirements.
OpenAI’s Dominance and the Risk of Vendor Concentration
Early ThreatLabz insights indicate that enterprise AI usage is highly concentrated among a small number of providers. This reflects how deeply AI capabilities are now embedded into enterprise software ecosystems and productivity environments.
While this concentration accelerates adoption, it also introduces systemic risk. Heavy reliance on a small set of AI providers increases exposure to:
- Service disruptions
- Policy changes
- Model updates
- Supply chain dependencies
For many enterprises, AI-backed services have effectively become part of core infrastructure – often without governance frameworks evolving at the same pace.
In many enterprises, this dependency is unmanaged because it does not map cleanly to existing vendor risk models. AI providers may not be contracted directly, yet they influence data handling, system behavior, and compliance posture. This makes traditional third-party risk assessments incomplete.
Help AG helps enterprises close this gap by combining AI asset discovery with extended risk models that capture AI dependencies. This enables organizations to assess AI providers based on their impact on data handling, system behavior, and compliance, addressing a critical blind spot in traditional assessments.
The Governance Challenge: AI That’s Hard to See
One of the most important signals emerging from early ThreatLabz research is how often AI usage occurs outside of clearly identifiable tools.
AI interactions are increasingly embedded within SaaS platforms, automated workflows, and background processes. As a result, AI-driven activity may blend into standard cloud traffic, making it difficult to detect or govern using traditional application-centric security models.
This lack of visibility creates tangible challenges:
- Sensitive data may be processed by AI-enabled services without explicit user intent
- Machine-driven interactions can bypass controls designed for human behavior
- Policy enforcement becomes reactive rather than preventative
As AI becomes an invisible operational layer, security leaders must rethink how governance and oversight are applied in practice.
Help AG addresses this challenge by helping enterprises shift governance from user-centric assumptions to activity- and data-centric control models. This includes identifying AI-driven interactions within cloud and SaaS traffic, mapping them to underlying systems and data types, and clarifying which policies should apply to machine-initiated actions.
By aligning governance frameworks to how AI-enabled systems operate, this helps organizations restore accountability and move enforcement upstream, reducing reliance on post-incident investigation and enabling more preventative oversight.
Engineering and IT as the Primary AI Risk Surface
When early ThreatLabz signals are mapped across enterprise functions, engineering and IT consistently emerge as the primary drivers of AI activity.
This is not surprising. AI is now embedded across development, operations, and infrastructure workflows that interact closely with proprietary systems, sensitive data, and intellectual property. Because these workflows are continuous and iterative, even small AI-enabled efficiencies can scale rapidly across teams and projects.
For enterprises operating in regulated and compliance-driven environments, this concentration raises important questions:
- How is AI usage governed across critical systems?
- Where does accountability sit for AI-driven activity?
- How do existing compliance frameworks adapt to machine-driven interactions?
AI-Driven Threats Are Evolving in Parallel
As enterprises operationalize AI, attackers are evolving alongside them.
Early observations suggest AI is being used to enhance traditional attack techniques through greater automation, scale, and targeting. At the same time, threat actors are beginning to explore weaknesses across AI supply chains including model dependencies, integrations, and data exposure paths.
This reinforces a critical point: AI risk is not limited to misuse or data leakage. AI systems themselves are becoming part of the enterprise attack surface.
Preparing for 2026: What Security Leaders Should Focus on Now
As enterprises accelerate their use of AI, one message is already clear. In working closely with Zscaler and observing these shifts across regulated environments, Help AG sees AI becoming foundational to enterprise operations – while security and governance models are still catching up with how it is actually used.
As organizations look toward 2026, security leaders should focus on:
- Understanding real AI usage patterns across the enterprise
- Identifying high-risk workflows and concentrations of AI activity
- Evolving governance models for automated, machine-driven interactions
- Aligning security, IT, and business stakeholders around shared responsibility
For regulated and compliance-driven environments, placing these global AI trends into local regulatory and operational context will be critical.
If you’re interested in receiving early access to the ThreatLabz 2026 AI Security Report or discussing how these AI security trends apply to your organization, contact Help AG to start the conversation.
