Data policy management in the financial industry is evolving fast.
Beyond being simply a compliance-led process, and toward becoming a competitive advantage. At least, it is for those organizations embracing automated policy enforcement.
Automation has long been a crucial tool for organizations wanting to take advantage of data’s increasing volumes, varieties and velocities. However, as data’s importance has grown, so too have the number of regulatory frameworks.
ISO27001, PCI, CIS, HIPPA, GDPR – these and many others pose governance-related questions. At industry level and international level. This year financial institutions are working out how to respond to the newly adopted Trans-Atlantic Data Privacy Framework. Analyzing the EU-US framework, EY points out:
“The European Data Protection Board recommends to banks, financial institutions, and insurance companies to identify and adopt supplementary measures that are necessary to bring the level of protection of the data transferred up to the EU standard of essential equivalence.”
Data policy management: Where regulatory change is the only constant
It’s not just about keeping pace with where and how data moves. It’s also about keeping pace with where and how data policies change, evolve, and are categorized.
For larger institutions in particular, manual data policy management processes are simply not practical. Especially when it comes to auditing, application and management – where visibility is required across the entire data lifecycle.
As financial industry incumbents have moved to compete with fintech disruptors, this has expanded data lifecycle attack surfaces. Challenges are underscored within financial services, where it’s still common to have legacy infrastructure “bolted onto” the cloud, rather than seamlessly connected.
Scalable resources are required to monitor each new service, application, and device. Such speed of innovation and DevOps is asking new questions of traditional data governance access control approaches. The answer is to be found in automated policy management.
“Technology executives face ongoing pressures to mitigate technical debt across legacy applications, devices, and computing infrastructure. Rapid development tempos, constant digital upgrades, and business agility add layers to the challenge.”Forrester
This can be a less complex technique than other methods. For example, Role-Based Access Control (RBAC) relies on predefined roles. Although for larger organizations, roles can evolve and change, often at short notice, which means more support requests routed to IT teams.
Attribute-based Access Control (ABAC) is more dynamic. However, it also requires more resources and processing. The risk of sprawl comes from a widening scope, with less visibility for systems and applications requiring this form of policy-based access control.
In contrast, automated enforcement means policies are monitored on a live and ongoing basis. A form of orchestration that mirrors the similarly dynamic nature of modern data. Just without the manual updates or extra provisioning more common with RBAC and ABAC.
Automated policy enforcement: How it works
Let’s take a brief look at how automated policy-based access management works in practice.
Part 1: Preparing organizational policies
We learn from historical requests – what was accepted, rejected, or accepted with limitation. Through this process, Velotix’s AI can build your policy catalog.
This will include policy identifiers, definitions, versions, URLs, positions in the data lifecycle and behaviors each request should automatically trigger.
Part 2: Enforcing automated policy management
Having set the policies and conditions, it’s now time to define enforcement actions. This requires input across departments, with stakeholders nominating policy owners and who should receive automated notifications, alerts and warnings.
Naturally, it also means making sure there is visibility for enforcement action. Of course, this depends on the system you use. However, a shared dashboard is the absolute minimum for building a single source of truth.
A centralized view can then support consistent workflows that can be scaled and adjusted. This makes it ideal for operating in complex and evolving data economies. For example, in the US with privacy laws that vary by state, compared to the more uniform approach seen across the EU-wide GDPR.
Part 3: Defining data engines, components & locations
Velotix can suggest policies based on past approvals or rejections. For example, factoring in that 85% of people previously accepted a rule, while 90% of people accepted the same rule alongside a different rule.
Events are stored and made available for monitoring and auditing. Policy requests, alongside exceptions or violations, are logged.
“By 2026, applying automated trust metrics across internal and external data ecosystems will replace most outside intermediaries, reducing data sharing risk by half.”Gartner
5 advantages of automated policy enforcement in the financial industry
Implementing automated policy management offers multiple advantages. These include:
Minimizing error & maximizing consistency
By automating routine policy enforcement, you’re also reducing the risk of alert fatigue within your team. They also have more time to proactively review your processes in order to identify alternative methods that reflect the fast-changing financial landscape.
Automation means consistency can be maintained – even at scale – while errors are minimized. What’s more, the organization’s people remain in charge of approvals and decision-making. However, instead of completing repetitive tasks with a higher risk of error, more time is available for them to add strategic value elsewhere.
Generating insights from a single source of truth
Policies can be centralized, with logic defined and executed in accordance with required regulations. No more duplication of files or inconsistent paper-based processes involving multiple sources. Automation can locate and map policies with compliance requirements, rather than teams having to sort through documents to prepare a manual risk assessment.
Organizations can be freed to innovate, secure in the knowledge that policies are upheld, while the business can stay up to date with evolving regulations.
Reducing bottlenecks also leads to more information-sharing within teams, boosting collaboration. Insights can be available in a few clicks, rather than sorting through files to gather information for presentations.
Gaining end-to-end visibility & transparency
As policies are added to the repository, metadata can be added automatically, improving search granularity and flexibility. This provides a clear and transparent way to assess environments, at any point in time, supporting the audit process.
Automated policy management also means data is no longer only in the hands of IT. Instead, data is unlocked for everyone in the organization. Meanwhile, automation will continually monitor, maintain and remediate.
This also makes it easier to check protocols are followed when any component, policy or app is added to the ecosystem. Audit trails can reveal when an enforcement occurred, if any exceptions were generated, and what action was taken by employees.
Saving time while boosting productivity
Automated policy enforcement means that the question “Can we authorize this access request?” can be answered by automatically routing requests. Instead of having to seek approvals through laborious data policy management processes and relevant stakeholders.
This also reduces the cycle time for identifying and fixing compliance issues. Automated changelog information can be consistent, repeatable and verifiable. Rather than waiting for teams to share data through spreadsheets or via other manual processes.
This is particularly crucial for financial institutions, such as those in the US under jurisdiction of regulations such as the Final Rule. This went into effect on April 1, 2022, and “requires a banking organization to notify its primary federal regulator of any “computer-security incident” that rises to the level of a “notification incident,” as soon as possible and no later than 36 hours after the banking organization determines that a notification incident has occurred.”
Continuous improvement with greater understanding
Here’s another crucial advantage.
Although it’s only available within platforms that offer dynamic workflow builders and an AI-driven policy engine. And there’s only one of those – Velotix.
Velotix gives you automated policy enforcement – backed by a patented AI engine that ensures continuous improvement. That’s because after every human confirmation of approval, any rule exceptions are noted, tracked, and added to future processes.
On one level you’re realizing the traditional benefits of automation. On another level, you’re also automatically optimizing your operations.
That’s because Velotix learns from historical requests, returning relevant and real-time recommendations based on attributes such as user, data type, business justification, and location.
Your teams gain more autonomy and access to greater understanding of enforcement within the business. What’s more, you start building a single source of truth that’s based on past actions within your business.
Where symbolic AI learns from historical behaviors, unique to your policy catalog requirements. Giving you real-time granularity for aggregation, enforcement, and building workflows.
All this (plus much more) is only possible with Velotix. Contact us today to learn more.