Automated policy enforcement is redefining data governance. Turning laborious manual processes into dynamic automatic workflows. Approving or denying access in minutes, instead of days or weeks. Learning and improving data policy accuracies and applications.
Of course, it’s not just automation that’s fueling this policy-based access management transformation. AI is at work too.Organizations successfully harnessing AI’s potential are already reaping the rewards. Accenture has identified a small group of these high performers. Labeled “Achievers,” they’re using AI to generate 50% more revenue growth.
“These Achievers are, on average, 53% more likely than others to be responsible by design. That means that they apply a responsible data and AI approach across the complete lifecycle of all their models, helping them engender trust and scale AI with confidence.”
Accenture
Behind the headline findings, it’s clear the technology tells only part of the AI growth story. Even if that story contains unprecedented possibilities, opportunities and use cases yet to be written.
Accenture also highlights how “high-quality, trustworthy AI systems that are regulation-ready will give first movers a significant advantage in the short term.”
The rise of these systems means more organizations can reach “Achiever” status. With quality and trust built in, businesses can practice what is known as “Responsible AI.” It’s simply a case of choosing the right platform and partner for the journey.
Automated policy enforcement & Responsible AI
Responsible AI is at the heart of successful data policy management. It’s divided into four principles:
Principle 1: Organizational
Employees should be able to easily raise concerns about AI. Whether there are risks around what the AI algorithm outputs, or how data is gathered, governed and shared.
Principle 2: Operational
Governance and compliance requirements should be clearly explained, with alignment between departments working with AI and data. Roles, structures and accountability should be established from the start, helping to build trust and confidence.
Principle 3: Technical
Technical methods should be available for implementing, defining and measuring use of AI, data and systems. These should create transparency for assessing the algorithm for fairness, possible biases and potentially discriminatory outcomes.
Principle 4: Reputational
Organizations should be able to communicate their AI vision to wider audiences. From internal stakeholders to external shareholders, offering a commitment to uphold fairness and transparency and ensure governance procedures are followed at all times.
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AI for regulatory compliance: From principles to practice
Through the regulation-ready systems highlighted by Accenture, organizations can ensure Responsible AI is applied to policy-based access control.
For example, by intelligently optimizing processes for defined rules and regulations. Configuring workflows for access requests that improve over time. And by maintaining visibility of AI operations, with team members still having the final say over decision-making. Rather than having to face an “AI black box” scenario with unknown algorithms and actions.
In other words, this approach unlocks the potential of AI in governance. In particular around transparency, visibility, and control of data. Automated policy management can then become a tool to compete globally. In two of the biggest international markets. That’s because AI is going to become very regulated, very soon.
The global perspective on AI regulation & responsibility
AI has already proven its capabilities across multiple industries. Whether for helping doctors diagnose cancer, supporting compliance teams to identify fraudulent transactions, or enabling utility companies to predict and model usage.
Its algorithms are fast expanding into the areas of deep learning and neural networks. Powered by quantum computing that offers unprecedented processing power. Capable of solving problems beyond today’s most powerful computers.
At the same time, there have been examples of biased data affecting AI and ML output. And cases such as these are sometimes complicated by the difficulty in finding out why an AI has arrived at a decision, prediction, or action.
“AI cannot thrive if the business does not trust AI techniques, so organizations need checks and balances to assess and respond to threats and damage and to ensure integrity is embedded into AI.”
Gartner
Ultimately, bias is part of human nature. That’s why systems offering discoverability, visibility and control of data can help solve these challenges. Freeing organizations to operationalize AI, while automating, aligning and delivering new standards of data access governance.
Data policy management: With great AI power comes great AI responsibility
The drive for Responsible AI is also coming from governments. Many are gearing up to regulate AI outputs and algorithms.
Within the EU, there’s the proposed Artificial Intelligence Act. It’s designed to align AI applications with EU values and citizens’ rights. The regulations will see AI grouped into four risk-based categories:
- Unacceptable risk
Where AI is being used to manipulate people, or target and cause harm to vulnerable groups - High-risk
This category is likely to see plenty of governance-related activity. That’s because it includes sensitive data that is most likely to require governance. For example, PII of people under criminal investigation, or travel documents relating to migration or refugee status - Limited risk
AI systems that require transparency and subjects to be informed and given options to opt in or opt out. For example, when sharing data with a chatbot or downloading a document that requires giving out their contact details - Minimal/no risk
AI-enabled video games or spam filters
The US has started along a similar path.
A proposed AI Bill of Rights contains five principles relating to the use of AI and automation. These have been developed to protect and guard citizens’ personal data. They also provide a clear framework to guide automated policy management implementations.
Below is a governance-oriented summary of the blueprint:
- Safe & effective systems
Automated systems should undergo monitoring to demonstrate their safety and effectiveness. This should include protecting citizens from inappropriate or irrelevant data use - Algorithmic discrimination protections
Automated systems should be designed and used in a way that protects against unjustified treatment of people. Whether that’s race, color, genetic information “or any other classification protected by law” - Data privacy
Only strictly necessary data should be collected, using permissions or alternative privacy by design safeguards. Whenever possible there should be access to reporting that confirm subjects’ data decisions have been respected - Notice & explanation
There should be clear descriptions and documentation relating to the role automation plays. Automated systems should come with timely and accessible explanations of any outcomes or impacts on people - Human alternatives, consideration, & fallback
Automated systems intended for use in sensitive domains should be tailored to specific purposes. Opting out should be available with the option of choosing a human alternative
Until these are ratified, there will be some uncertainty. However, for organizations to succeed with AI, it’s a case of when, not if, Responsible AI is implemented. Delivering compliant AI systems now, before legislation is enacted, offers that crucial first-mover advantage.
Automated policy enforcement with AI: What to look for
Responsible AI starts with a responsible automated policy enforcement provider. Providing a platform to prepare your organization for what’s on the horizon. Here are some areas to consider and prerequisites to look for:
Unified discoverability
Organizations need to refine their vast volumes of data, for visibility and accessibility. Data catalogs should be aggregated – to automate, cleanse and unify metadata for a standardized taxonomy. Through a single source of truth, it then becomes possible to understand transformations and links between variations of your data.
Real-time access
Data should be available in real-time. Along with automatic routing to your relevant stakeholders, removing bottlenecks arising from manual approvals. Controlled and configured access should mean no need to move, replicate, or transform data.
Enforced security
There should be automatic data privacy control, to securely manage PII and automatically enforce viewing restrictions. This should include multiple data anonymization and obfuscation methods, freeing your employees to focus on extracting value and sharing insights responsibly.
Intelligent automation
Harnessing AI goes hand-in-hand with automation. Policy management should be consolidated, converged and dynamically adjusted and aligned. Building your organization a centralized repository for rule-based automated policy management.
Controlled lineage
Data should be easily followed throughout its lifecycle. That means establishing data lineage for identifying transformations and relationships between datasets. Giving you visibility and control, for tracking and auditing permission statuses.
Dynamic self-service
Organizations should have a centralized platform for sharing structured and unstructured data responsibly. This should remove the need for replication, while empowering employees to use refined data. By sharing from one platform, this will open up the ability to integrate your BI tools.
Getting started with AI & automated policy management
Incorporating AI for regulatory compliance will pay dividends – in two ways.
First, through the already-recognized benefits of successful AI adoption. For example, the ability to scale quickly, continuously improve with enriched data, and transform your processes.
Second, by turning data governance into competitive advantage. Where AI-powered automated policy enforcement helps you democratize, enrich and share data-driven insights faster and more securely.
To achieve this reality, we’ve built a revolutionary data protection and access permissions platform.
You get automated data access management, through the only AI-based data security platform that automatically grants the right access, to the right data, for the right people, at the right time.
Velotix’s patented AI engine learns from your historical requests. Providing cybernetic recommendations based on users, data requested, justifications and locations.
The AI tracks rule exceptions, building and updating policies, for granular visibility. No need to manually track and record policy implementations.To bring all this to your business, and incorporate Responsible AI into your data access governance, contact us to get started.
