AI workflows - RecordPoint: Accelerating AIOps with integrated data risk management

Fragmented data ecosystems

Most enterprises run hybrid architectures: multiple clouds, on‑prem systems and dozens of SaaS apps. These structured and unstructured data sources each have different defaults for data collection, storage and retention. Today, many SaaS tools will now ship with their own LLMs/agents, creating AI silos without centralised oversight.

Then there’s dark data, the data you don’t know you have, but are still responsible for. Unclassified, unmonitored and over-retained data increases exposure and can make its way into an AI model, potentially leading to substandard or noncompliant output. A subcategory of dark data is ROT, redundant, obsolete and trivial data clogging up your systems.

Organisations are facing evolving regulatory requirements, from the familiar – GDPR, CCPA, HIPAA and industry-specific rules – to the AI-specific standards: the NIST AI RMF, ISO/IEC 42001 and the EU AI Act.

DevOps/AIOps bottlenecks

When you can’t be sure you understand your data and you have an increasing collection of regulatory standards to meet, you wind up with security and compliance checks that slow down developer velocity without automation. Or worse yet, you don’t have any checks… and the risk continues to build in the background.

So then, let’s get to the case for integrated data risk management in AIOps.

By treating operations and governance as separate issues, you increase risk and slow delivery. Developer velocity may increase, but the overall production process won’t: these security or compliance checks need to happen or you risk a data leak or a fine.

Say goodbye to after‑the‑fact checks and audits, emergency meetings and waiting for compliance sign-off and instead, usher in continuous classification, retention enforcement and contextual risk scoring, which will raise early warnings, prioritise action and create auditable trails in real time.

By establishing a data risk management system up-front and setting policies ahead of time, you will avoid the need to solve these problems in the heat of development, leading to slower deployment and potentially inconsistent policies. Tools like Sonar Cloud allow you to move the compliance and risk management stage all the way left, into your CI/CD process to, for example, block a code merge if it would be against a policy. That’s about as far left as you can move these processes.

Bridging operations & governance

By embedding risk detection into AIOps workflows – observability, incident management and platform automation – you’ll get consistent controls where the work actually happens.

No longer is it a choice between speed and safety. Embedding data risk management in the development process allows you to shrink your risk surface while maintaining or improving delivery speed. With the right controls, teams ship faster because fewer changes are blocked late in the delivery process.

Data classification and risk detection is key to accelerating processes. The core of the solution is data and ensuring you know what you have, so you can make sure it’s compliant, securely held and appropriate for use in an AI model. If you can continuously manage compliance and security at the level of the data, you can avoid slowing down developer processes.

Automated classification

Automated classification allows you to identify sensitive data types like personally identifiable information (PII), payment card information (PCI), or protected health information (PHI) at scale, across all your apps. This classification doesn’t need to be done by an LLM. Classification should run continuously and be context‑aware.

Detecting and defensibly deleting or archiving your redundant, obsolete, or trivial data (ROT) reduces exposure, storage cost and the likelihood that sensitive or low‑quality data contaminates model training.

A data‑centric approach ensures only fit‑for‑purpose data enters a model. Guardrails block confidential or regulated information from training sets and from prompts/responses when interacting with LLMs and agents. Not all findings are equal. Prioritise based on data sensitivity, location, user access patterns, business criticality and the regulatory context of the affected systems. Strategic benefits for CIOs and CISOs. Fewer high‑value data stores left unclassified or over‑retained mean lower probability and reduced impact of breaches.

Less manual oversight lets platform teams manage controls centrally while developers self‑serve. Continuous evidence  – policy execution logs, exception audits and control coverage  – translates into cleaner audits.

Future‑ready for agentic AI

As models move from recommending to acting, guardrails anchored in data governance become non‑negotiable. AIOps isn’t going anywhere, but before organisations dive into this new paradigm headfirst, they should take time to understand the data risk and work on reducing it. 

By prioritising integrating data risk management AIOps, organisations can get the best of both worlds: optimising performance but also safeguarding the enterprise. By considering the data, setting policies in advance and shifting the burden of compliance further left, organisations can speed up delivery and reduce the risk of a compliance or data breach.

RecordPoint’s Woodward: Embedding data risk management in the development process allows IT teams to shrink their risk surface while maintaining or improving delivery speed… (Ed: sage advice, but mind that nice suit on the grubby wall).