Article
2 min read
Andy Rossiter
  • Global SVP of Google Cloud at Endava

The promise of Artificial Intelligence is transformative. With platforms like Google Vertex AI, developers can unify the ML workflow, accelerating model creation from weeks to days. Yet, for many organizations, the journey stalls not on technical capacity, but on security and governance. 

 

This is the AI Security Paradox: the speed demanded by innovation clashes directly with the diligence required for compliance and risk management. For CISOs and IT leaders, the adoption of Vertex AI – and the MLOps pipelines that power it – introduces three critical blockers:  

 

  1. The sensitive data magnet 

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AI is data-hungry, making the cloud environments that host Vertex AI workloads the ultimate target. A single environment can contain training data sets holding personally identifiable information (PII), protected health information (PHI) or proprietary business intelligence. 

 

  • The risk:  Data leakage during training, improper access controls on storage buckets (GCS or BigQuery) or unauthorized inference requests that could potentially leak sensitive information. Without rigorous access controls and monitoring, these environments become massive, high-value security risks.  

 

  1. Model integrity and trust 

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The model itself is the intellectual property, but its function must also be trustworthy. The complex, third-party-dependent nature of MLOps introduces supply chain vulnerabilities and new attack vectors. 

 

  • The risk:  Data Poisoning (corrupting the training data to introduce bias or flaws) or Model Theft/Evasion (stealing the trained model or crafting malicious inputs to trick it). These threats compromise the integrity of the AI's output and the competitive advantage it provides.  

 

  1. The black box governance problem

     

Governance in AI is challenging because the decision-making process can be opaque. When moving fast, ensuring auditability and compliance across the entire ML lifecycle – from code creation to model serving – is difficult. 

 

  • The risk:  Lack of visibility into the security posture of the underlying cloud resources (GKE, Compute Engine) that power Vertex AI. Security teams struggle to understand who has access, what resources are misconfigured and whether the MLOps pipeline itself contains vulnerabilities. This complexity slows down compliance audits and blocks fast-paced deployment. 

 

The path forward  

 

The solution is not to slow down innovation, but to implement a unified security architecture that automates visibility and threat response across your entire Google Cloud environment. 

 

You need tools that specialize in both proactive prevention of the risks discussed above and real-time detection of ongoing threats. 

 

In the next post, we will introduce the two powerful security accelerators – Google SecOps and Wiz – that are purpose-built to resolve this paradox and accelerate secure Vertex AI deployment.