Intelligent automation - The AI & Generative AI process wrapper

In this post, we will explore how enterprises can harness the transformative power of AI and Generative AI while simultaneously addressing the very real concerns of bias, hallucinations, and data security by using intelligent automation. We will look at how to enable the responsible and effective AI deployment.

Introduction

In an era where data reigns supreme, it is an undisputed fact that enterprises across industries are increasingly drawn to the transformative capabilities of Artificial Intelligence (AI) and Generative AI. These technologies promise to enhance productivity, streamline operations, and enable data-driven insights. However, amidst the enthusiasm for AI & Generative AI in particular, there is a big undercurrent of nervousness. Enterprises are struggling with legitimate concerns, particularly regarding the fears of bias, hallucinations, and the safeguarding of customer and private data.

While AI, including Generative AI, offers a plethora of benefits, its rapid adoption raises essential questions. Enterprises are eager to leverage AI to gain a competitive edge, but they are also wary of the potential pitfalls. Let's examine some of the key fears that quite rightly restraining enthusiasm:

Bias and Fairness: AI models are only as good as the data they are trained on. Biased training data can result in AI systems that perpetuate and even exacerbate existing biases. Enterprises fear that the AI they adopt may make biased decisions in areas like hiring, lending, or criminal justice, potentially leading to discrimination and legal liabilities.

Hallucinations and Unintended Creativity: Generative AI, in particular, has garnered attention for its ability to create content, whether in the form of text, images, or audio. However, the fear here is that these systems may generate misleading, inappropriate, or false information that could harm an organization's reputation, confuse customers, or spread disinformation.

Data Privacy and Security: The collection and usage of customer and private data is at the heart of many business operations today. Enterprises are increasingly concerned about the security and privacy of sensitive information. There are concerns about data breaches, unauthorized access, and the potential misuse of customer data, leading to loss of trust and regulatory repercussions.

Explain-ability and Accountability: AI, especially deep learning models, often operate as "black boxes," making it challenging to understand how decisions are reached. Enterprises are concerned about their ability to explain AI-driven decisions to regulators, customers, and stakeholders, and about the potential lack of accountability when things go wrong. An audit trail is also required to allow enterprises to show the process trail.

Navigating the AI Frontier Safely

Despite these concerns, the allure of AI and Generative AI remains strong. Enterprises recognize the potential to revolutionize processes, unlock insights, and drive innovation. However, these technologies must be embraced with a combination of enthusiasm and caution or control.

Addressing the fears surrounding AI requires proactive steps. This involves transparent model development, rigorous data validation to mitigate bias, robust cybersecurity measures, and a commitment to ethics and compliance. The responsible adoption of AI, underpinned by comprehensive governance and auditing mechanisms, can help mitigate these concerns.

So basically, it’s is all about the data…?

So if it is all about the data what should enterprises be looking at?

A lot of this comes down to the triad of Data Integrity, Control, and Auditability and all of this needs to be enclosed into a process wrapper and it is Intelligent Automation that can provide that wrapper.

The Three Pillars of Data Governance

Organizations rely on data for informed decision-making, regulatory compliance, and operational efficiency, this is no different when looking at feeding AI. The integrity, control, and auditability of data are paramount for ensuring trust, transparency, and accountability. As the scope and complexity of data continue to grow, the need for robust data management solutions has never been more critical. In this context, Intelligent Automation should be seen as a powerful frameworks that can underpin data integrity, control, and auditability, because at it’s very heart it is rules based!

There are a number of areas to consider:

Data Integrity

Refers to the accuracy, consistency, and reliability of data throughout its lifecycle. It involves ensuring that data remains unchanged and authentic, preventing unauthorized tampering or corruption. When using Generative AI services, Intelligent automation can play a pivotal role in upholding data integrity through the following mechanisms:

Data Validation and Verification: Digital Worker’s when combined with Generative AI, can cross-verify data across various sources, identifying anomalies or discrepancies quickly and even generating synthetic data for validation purposes.

Data Cleansing:  Using Digital Worker’s you can building a process wrapper that can automate the guardrails around data cleansing. Using machine learning models with Digital Workers allows you apply a rule-based processes, allow you to identify data inconsistencies and errors, which can can be rectified promptly by the Digital Worker or a human in the loop to maintaining its integrity.

Encryption and Access Control: Intelligent automation can enforce robust encryption and access controls to protect sensitive data. Digital Worker’s can use Generative AI to creating synthetic data for testing therefore allow you to run tests without compromising actual data security.

Data Control

Data control encompasses the management of data, including who has access to it, what they can do with it, and the enforcement of data governance policies. Intelligent automation combined with Generative AI bring a new level of control to data management:

Automated Workflows: Intelligent automation can create and enforce workflows (the Process wrapper) that dictate how data is used, processed, and stored, reducing the risk of unauthorized changes.

Data Masking and Redaction: Sensitive data can be automatically masked or redacted when accessed by individuals when using forms and human in the loop where the necessary permissions are not in place or there are safeguarding privacy, and maintaining control. Digital Worker’s can use Generative AI can generate synthetic data in place of real data, allowing controlled access without exposing sensitive information.

 Data Auditability

Data auditability is the capacity to trace and monitor every interaction with data, creating an audit trail for accountability and compliance purposes. Intelligent automation enhance data auditability in the following ways:

Logging and Monitoring: Intelligent automation can maintain comprehensive logs of process data and will allow you to understand who accessed, changeed, and  interacted system, while Generative AI services can generate synthetic logs for testing and validation, providing a transparent record of data usage.

Real-time Alerts: Digital Workers can create alerts that can be configured to notify stakeholders of any suspicious or unauthorized data access or changes, enabling swift response and investigation. Digital Workers can feed alert data to Generative AI tool to assist in anomaly detection by identifying unusual patterns in data access.

Compliance Reporting: Intelligent automation can generate compliance reports automatically, saving time and ensuring adherence to industry regulations and standards. Generative AI can facilitate the creation of compliance reports by generating synthetic data samples that demonstrate adherence to regulations.

Benefits of Intelligent Automation and Generative AI in Data Governance

Efficiency: Intelligent automation and Generative AI significantly reduce the manual effort required to manage data, allowing organizations to allocate resources more effectively.

Consistency: Intelligent automation  ensures that data governance policies are consistently applied, reducing the risk of human errors. Generative AI services with the guardrail of intelligent automation can ensure the consistency of data generation and testing processes.

Security: With Digital Workers you get encryption, access controls, and monitoring, data security is strengthened, and the risk of data breaches is minimized. Intelligent automation can be used to look at data testing and validation using Generative AI to create secure without exposing sensitive information.

Compliance: By automating compliance reporting and audit trails, organizations can demonstrate their commitment to regulatory compliance, and once again Intelligent automation powered Generative AI services can assist in generating compliant synthetic data for testing and validation.

Conclusion

In the era of big data and increased digitalization, data integrity, control, and auditability are non-negotiable for organizations when using it to feed AI models. Consuming Generative AI through the process wrapper of Intelligent automation serves as the linchpin of effective data governance, enhancing the accuracy, security, and accountability of data throughout its lifecycle. As organizations continue to invest in these technologies, they are not only future-proofing their data management but also ensuring that they can make well-informed decisions while maintaining trust and transparency in their operations by using intelligent automation as the process wrapper. The combination of Intelligent Automation and Generative AI represents a powerful synergy that can revolutionize data governance in the digital age.