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3 Big AI Risks  and How Platform Engineering Can Help

3 Big AI Risks  and How Platform Engineering Can Help

Artificial Intelligence isn’t just a part of the future; it’s a permanent fixture in the present. From predictive analytics that know what you want before you do, to customer service bots that are slightly less grumpy than a Monday morning commuter, AI is revolutionising how businesses operate. But beneath all this shiny new innovation lies a slightly less glamorous truth: AI introduces new risks that many organisations are about as prepared for as a tourist standing on the right of a TFL underground escalator.

AI doesn’t run on its own. It runs on platforms. And this is where platform engineering becomes mission-critical, not just to enable developers, but to protect, govern, and scale AI responsibly. It’s the digital equivalent of a good pair of walking boots — it might not be the most exciting part, but it’ll stop you from slipping in a rogue puddle of mud or a security breach.

In this article, we’ll unpack three major AI risks that threaten operational resilience and data security, and explore how platform engineering provides the necessary controls to keep AI environments safe.

Risk #1: SaaS Sprawl Is Your Biggest Blind Spot

Modern AI workflows are built on a patchwork of third-party services, from data labelling and orchestration tools to vector databases and model deployment platforms. These Software-as-a-Service (SaaS) components accelerate AI development, but they also introduce major vulnerabilities. It’s like inviting a whole host of new neighbours to your street party — some are perfectly lovely, but you have no idea whether, when drunk, one of them is going to leave an unwanted gift amongst the magnolias.

Many AI stacks depend on SaaS providers that:

  • Expose insecure or undocumented APIs
  • Have poor identity and access management
  • Lack of visibility into how data is stored or processed

Why This Matters:

You might trust your own code — it’s your baby, after all. But can you trust every single vendor your AI stack touches? A single compromised API or poorly configured integration can give attackers a pathway straight to your data, models, or internal systems. It’s the digital equivalent of leaving your back door ajar and hoping no one notices.

 

How Platform Engineering Can Help:

Let’s first start with a brief summary of the Swiss army knife of a tool for a platform engineer — the Internal Developer Platform (IDP). These generally act as a single entry point and pane of glass for developers, abstracting away the underlying complexity of cloud infrastructure and security tools. By building an IDP, the platform team and other stakeholders can embed best practices directly into the developer workflow. This moves the responsibility of enforcing security from individual developers to the platform itself. The IDP becomes the central hub for managing all aspects of the AI environment, from infrastructure provisioning to third-party integrations.

 

Zero-Trust SaaS Integration: 

  • Instead of a developer manually setting up a new SaaS integration and its credentials, an IDP automates this process. The developer simply uses a self-service portal to request access to an approved SaaS tool. The platform then uses policy-as-code to automatically:
  • Provision a secure integration: It provisions a secure API gateway endpoint and generates short-lived credentials from a secret management tool like HashiCorp Vault.
  • Enforce least privilege: The platform automatically configures credentials to have only the necessary permissions for the specific task requested by the developer, adhering to the zero-trust principle by default.

 

Continuous Monitoring and Auditing: 

  • An IDP integrates with monitoring tools to provide a unified view of the entire AI stack.
  • Automated Mapping: The platform uses a Cloud Security Posture Management (CSPM) tool to continuously discover and map all third-party integrations. This data is then displayed in the IDP’s dashboard, giving the platform team and developers a real-time, consolidated view of their SaaS ecosystem.
  • Drift Reconciliation: The IDP continuously checks the deployed environment against its desired state, as defined in code. If a configuration change (drift) occurs, such as a developer manually granting excessive permissions to a SaaS tool, the platform can automatically flag it or even revert the change. This proactive approach ensures compliance is maintained in real-time, moving beyond static, point-in-time audits.

Automatic High-Risk Tool Blocking: 

  • The IDP acts as a controlled gateway for all tools and services.
  • Policy-as-Code Enforcement: The security team defines a set of security baselines using tools like Open Policy Agent (OPA). These policies are embedded in the IDP’s deployment pipeline.
  • Pre-emptive Blocking: When a developer attempts to integrate a new, unapproved SaaS tool, the IDP’s pipeline automatically runs the policy check. If the tool lacks essential security certifications or features (e.g., encryption), the IDP automatically blocks the integration. The developer receives immediate feedback, preventing the high-risk tool from ever entering the production environment.

 

Risk #2: Compliance ≠ Security

SOC 2, ISO 27001, GDPR — these certifications may appear impressive and certainly look good on a digital wall, but they don’t guarantee that your AI systems are secure. They offer point-in-time assurance, but AI environments evolve far too rapidly for annual audits to keep up. It’s a bit like checking if your car is roadworthy once a year and then carrying on driving when you get a puncture.

The Reality:

Security certifications are useful, a bit like a well-meaning safety manual. But attackers don’t care about checkboxes. They exploit misconfigurations, shadow access, overly permissive policies, and unmonitored components. They’re more interested in the digital chaos you’ve created than your paperwork.

 

How Platform Engineering Can Help:

A core principle of platform engineering is moving from static, point-in-time checks to continuous, automated validation. An Internal Developer Platform (IDP) provides the perfect mechanism for this, embedding security directly into the development and deployment lifecycle. By abstracting the underlying infrastructure as code, the IDP empowers developers to define their needs using configuration-as-data while the platform handles the secure provisioning.

 

Continuous Validation and Drift Reconciliation: 

  • An IDP integrates with your monitoring and infrastructure management tools to ensure that your environment always matches the desired state. The IDP would apply:
  • Configuration-as-Data: The developer uses a simple, high-level configuration file (e.g., YAML or JSON) within the IDP. This file specifies the desired state of their application, such as required resources, dependencies, and settings. The developer doesn’t interact with complex infrastructure code directly.
  • Abstraction Layer: The platform team defines and maintains the underlying Infrastructure-as-Code (IaC) templates (e.g., in Kubernetes Operators, Terraform or Pulumi). The IDP takes the developer’s configuration-as-data file and uses it to automatically populate and execute these IaC templates. This ensures that all infrastructure is provisioned securely and consistently, following predefined best practices.
  • Automated Reconciliation: The IDP continuously compares the live environment against the configuration defined in the developer’s data file and the underlying IaC. If it detects configuration drift — for example, a developer manually changes a cloud storage bucket’s permissions outside of the standard process — the IDP can automatically revert the change or alert the platform team. This ensures compliance is a constant state, not a snapshot.
  • Continuous Monitoring: An IDP integrates with monitoring solutions such as Prometheus, Grafana or Zabbix. These tools provide real-time visibility across data pipelines, models, and APIs.

 

Thinking Like a Red Team with the Platform: 

  • An IDP allows the platform and security teams to build security gates and simulations directly into the platform, empowering developers to build resilient systems — processes such as:
  • Chaos Engineering: Tools like Gremlin or Chaos Mesh can be integrated into the IDP to simulate internal attacks and failures. The platform team can define “game days” where these tools are used to test the resilience of the AI pipelines in a controlled environment.
  • Automated Security Scans: The IDP’s CI/CD pipelines can include automated security testing tools like SonarQube or OWASP ZAP. These tools scan code for vulnerabilities and misconfigurations before it’s ever deployed, shifting security left in the development process.

 

Define Security as Code: 

  • This approach automates the enforcement of your organisation’s controls, ensuring that every change adheres to your security and compliance policies. The platform team would look to enable:
  • Policy-as-Code (PaC): Using tools like Open Policy Agent (OPA), allows the security team to write policies that define what is and isn’t allowed. For instance, a policy might state that “all data storage buckets must have encryption enabled” or “no public-facing API endpoint can be deployed without a firewall.”
  • GitOps Workflow: The IDP enforces a GitOps workflow, where all changes to the configuration-as-data files must be made via a pull request to a central repository. Before the changes are merged, the IDP’s CI/CD pipeline automatically runs the OPA policies. If the proposed change violates a security rule, the pipeline fails, and the change is blocked. This ensures that every deployment is a secure deployment.

 

Risk #3: Cloud-First Doesn’t Mean Control-First

Public cloud platforms offer speed, scale, and convenience, especially for AI workloads requiring large compute resources. But there’s a catch: visibility and control degrade quickly. Just like a massive house party — it’s brilliant fun, but you still have no idea who’s in the kitchen. Maybe it’s those new neighbours again, and they’ve eaten all the lemon drizzle cake you made for the school fair.

When you fully depend on managed services or proprietary infrastructure, it becomes difficult to:

  • Monitor exactly how and where data is processed
  • Define consistent access policies across services
  • Detect misconfigurations or silent failures

This is especially problematic for regulated sectors (finance, healthcare, public services) or where data sovereignty matters.

How Platform Engineering Can Help:

A platform engineering approach, underpinned by an Internal Developer Platform (IDP), restores control and visibility by creating a standardised, governed layer on top of public cloud services. This ensures that while developers can leverage the cloud’s power, they do so within safe, pre-defined guardrails.

 

Embrace Hybrid- or Multi-Cloud Strategies:

  • A platform team can provide a unified experience for developers regardless of the underlying cloud provider. This allows the business to place workloads where it makes the most sense.
  • The IDP provides a single self-service interface for developers to deploy their AI workloads, abstracting away the specifics of AWS, Google, Azure, or an on-premises data centre.
  • The Cloud/SRE teams can use Kubernetes operator CRDs, Terraform or Pulumi templates to define the infrastructure for each environment. The IDP then allows developers to choose where to deploy, while the platform ensures that the underlying infrastructure is configured correctly and securely, whether it’s in the public cloud or on private hardware.
  • For sensitive data, the IDP can enforce policies that automatically route workloads to a private cloud or on-premises infrastructure, ensuring full control over identity, encryption, and data access.

 

Enforce Full-Stack Observability:

  • An IDP centralises monitoring and logging, giving platform teams and developers a complete, end-to-end view of their AI pipelines.
  • The IDP automates the deployment of observability agents (Prometheus, Grafana, or Zabbix) across all environments, regardless of the cloud vendor. This standardises how metrics, logs, and traces are collected.
  • The platform provides a single dashboard within the IDP, allowing developers to see the health and performance of their AI models from data ingestion all the way through to model inference. This helps to detect anomalies, security threats, or silent failures quickly, rather than having to piece together information from multiple, disparate provider consoles.

 

Set Your Own Cloud Governance Standards:

  • Instead of relying on a cloud provider’s default settings, the platform team uses an IDP to enforce a consistent set of security and governance rules across all environments.
  • The security team defines security baselines and policy-as-code using tools like Open Policy Agent (OPA) or HashiCorp Sentinel. These policies are built directly into the IDP’s deployment pipelines.

The IDP acts as the single point of control, ensuring that every piece of infrastructure or application deployed adheres to the organisation’s specific governance rules. For instance, a policy might prevent the deployment of a public-facing database or enforce specific encryption standards for all storage buckets. This prevents human error and ensures that the company’s controls are always upheld, regardless of the cloud provider’s defaults.

 

The Strategic Role of Platform Engineering in AI Risk Management

Security and governance are not the enemy of innovation — they’re what make safe, scalable innovation possible. Platform engineering sits at the intersection of infrastructure, compliance, and delivery — giving your teams the tools to control risk without slowing down development. It’s the sensible adult in the room, making sure everyone has fun without setting the house on fire.

 

Final Takeaway: You Can’t Secure What You Don’t Control

The hidden complexity of AI infrastructure creates opportunities for attackers — and headaches for engineering teams. Without control, observability, and governance, AI becomes a liability.

Platform engineering ensures your organisation doesn’t just build fast — it builds responsibly. By owning the infrastructure layer, platform teams become the first line of defence against AI risk.

Governance, Security, Innovation — Without Compromise
With Mesoform’s platform engineering expertise, you don’t have to choose between moving fast and staying secure. Feel free to get in touch