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RPA and GDPR: Security Governance in the Automation Era

RPA and GDPR: Security Governance in the Automation Era

The data on security breaches is overwhelming on many fronts. Over a billion records of consumers have been compromised since 2005. The total number of breaches in the period is threatening at around 8000. As late as 2017, big companies like Target, Equifax, and Neiman Marcus could not shield themselves from data breach attempts. Mind you, one of these is a top national credit reporting agency.

Noted analyst Avivah Litan predicts the following instances of misuse for the stolen data:

  • The data can get tossed around in an endless sell-and-resell loop of underground data piracy
  • Sensitive data can be used to steal bank accounts from customers
  • Identity thieves can use the data to update their existing records of targeted individuals
  • Adversarial nation states can use the data to disrupt peace or launder money out of the US

None of these constitutes stray casualty. The cumulative implications of the breaches are beyond grave. In fact, it is very difficult to quantify the damage dealt by these breaches to the society at large. That is where the General Data Protection Regulation (GDPR) swings into action. It gives consumers greater control over their own data while making corporates bite the bullet on their data processing practices.

What is the GDPR?

The GDPR is a regulation adopted by the European Union. It lays out the norms for data protection and privacy for the individuals that live in the European Union. It is one among the series of regulations that have helped formalize governance around security concerns of the average consumer.

In addition to strengthening consumer rights, GDPR aims at formalizing security standards that companies must establish to protect the data of their consumers.

Every organization functioning out of Europe and non-European organizations that collect the data of European citizens are expected to comply with the GDPR. The latest GDPR guidelines regulate how personal data is used, processed, stored, and deleted.

The GDPR also lays out that data subjects can request for both access and real-time usage information from organizations. If there’s any breach involving the personal data of users, it must be reported to the appropriate authority that oversees the regulation.

Security Governance: The Onus is on the Enterprise

At the crux of the GDPR is the impetus the regulation puts on enterprises to do all things necessary to protect consumer information. This has forced every enterprise software vendor to re-evaluate their policies regarding storage and management of sensitive user data.

This is where Robotic Process Automation (RPA) is impacting the industry in a big way. RPA platforms like Automation Anywhere are instrumental in offering comprehensive features in security and reliability. Starting with automation at once promises the following benefits for organizations:

  • Data encryption at all levels – when the data is in memory, in motion, or at rest.
  • A robust security framework (either built-in or third party) that guarantees security in the management and storage of user information. As a default practice, machine that store user credentials meant for critical purposes and the machines that run the software should always be exclusive.
  • Analysis of codes on both static and dynamic parameters, including manual pen testing for unbreakable application security.
  • Seamless enterprise based authentication system integration
  • Expansive logs of audits to support forensic analyses and audit processes
  • Secure operations that that make sure data is not exposed to business process threats during standard execution of processes

RPA platforms work with many ERP tools and in effect touch extensive sets of data within your organization. In case you are already using an RPA platform, make sure to check with them on GDPR compliance and the security measures they follow to ensure compliance.

How is RPA Easing up GDPR Implementation?

The first and absolutely unavoidable threat with manual processing of customer data is the guarantee of human errors. It does not really matter what level of security you follow. Even the slightest margin of error means that the organization is at the risk of non-compliance.

With RPA, you can automate the process defined by the legal and business teams to become GDPR compliant. Here is a collection of ways in which bots are helping enterprises with GDPR compliance:

Audit Logs

Enterprise RPA platforms are loaded with audit logs which monitor every operational process, creating logs for users and events at every stage of a given process. When there’s a data breach, audit logs swing into action with recurring spells of root cause analysis. What follows is routine forensic analysis to recognize and thereafter report the breach.

Content that relates to specific internal or external events can be gathered concurrently in real time. This comes in especially handy in case an organization is attempting to decode a fraudulent activity.

Documentation of data

There’s a lot of data pouring in from devices, sensors, and systems at the office. From the organizations perspective, it must be able to document all the data that is held in its directory, along with the source of its origin. The organization must be able to submit updated reports to the authorities in charge of data protection. GDPR mandates companies to purge personal data once it has crossed the holding period.

This is another area where RPA can help organizations by using bots that automate the process of masking PII data that is identifiable across applications. For the PII data that does not adhere to established policy, Natural Language Processing (NLP) lets bots recognize such data and generate alerts that help in intercepting the issue.

Data Breaches

GDPR makes it mandatory that subjects affected by data breaches be informed about it within 72 hours. For data breaches of a magnanimous nature, sending out information to everyone involved within 72 hours can become almost impossible. Imagine the case of Equifax, where 143 million users directly affected by the breach.

On the flip side, it is way easier to automate software bots to perform the job. In most instances, it does not even take 72 hours and makes sure the security governance timeframe is met.

Right to Access Information

European customers can request to access their information and know how an organization stores and uses the information. GDPR guarantees this right to all European consumers. If an organization wants to do this manually, it would need a dedicated team of individuals. Plus, every individual on the team must have access to such information.

It is way easier for bots to navigate through different systems and pull out data relevant to every user in question.

Right to Information Deletion

If a user requests an organization to dispossess their personal information, GDPR mandates the organization to delete such information promptly. Consider there is no automated process to do this. An employee or a team will have to access the information and then delete it from dozens of applications. Bots can not only pull out the relevant information on users but also email the report back to the concerned customers.

Some Data Cannot be Seen

There are legacy systems hiding data more than a decade old. Data can be accessed from these systems when needed. However, it’s never been as important to uncover sketchy data as it is now. RPA is the most convenient way to integrate the current technology platforms with legacy systems. Automation is also perhaps the only way to document and recognize available data that might be the cause of non-compliance.

Most companies are still taking their own sweet time understanding and dissecting the General Data Policy Regulation. At this, there is the threat of flooding of requests by consumers. Adhering to these requests will be compulsory. Doing it manually will mount up heavy costs on the administration. But the fact is responding to such requests might only be subject to a few well-defined requests. That makes it a great process for RPA to flex muscles.

The crux of it is organizations will have a hard time maintaining GDPR compliance in the absence of RPA. RPA solves security governance through GDPR wholly with the promise of zero errors.

Industry 4.0: The Era of Cyber-Physical Systems and Intelligent Analytics

Self-driving cars. Intelligent bots. Neuro-tech psycho-development. Genetic editing. Geoengineering. Artificial lifeforms. Mobile supercomputing of a surreal standard. Welcome to Industry 4.0.

Industry 4.0 or the Fourth Industrial Revolution is not a purely industry-specific phenomenon. Like the three industrial revolutions of the past (water and steam-powered motors, electricity-powered assembly lines, and computerized systems), the fourth industrial revolution will challenge the very way we work, live, and connect with one another – and with bots. The adoption and amalgamation of distinct values like the internet of systems, internet of things, and the cyber-physical grid will redefine and reform the very fabric of industry as we know it.

First off, we need to understand that Industry 4.0 or the Fourth Industrial Revolution is not a gimmicky marketing stunt pulled off to sell a new technology. It’s a living phenomenon described in very fine detail by Professor Klaus Schwab, founder and executive chairman of the World Economic Forum in his book, The Fourth Industrial Revolution. In his book, Professor Schwab argues:

The changes are so profound that, from the perspective of human history, there has never been a time of greater promise or potential peril.

While Schwab highlights that the dawn of the fourth industrial revolution promises exponential growth, he also points out a set of risks that constitute the “potential peril” in the above statement. We’ll get to that in a minute.

What constitutes Industry 4.0?

By now, it is understood that both computing and automation have leaped to the next level with Industry 4.0. Core principles of robotics are connected to computer systems, which are in turn equipped with ML (machine learning) algorithms. The resultant loop can learn, control, and execute robotics and allied operations with minimum (almost negligible) human supervision.

Then, there’s the introduction of what is called the “smart factory.” Within the setup of a smart factory, cyber-physical systems have the capability to make decentralized decisions while monitoring physical processes. The wireless web connects the physical systems enabling interaction with one another and with humans.

By this structure, a setup can be considered within the Industry 4.0 structure when it has the following four attributes:

  • Independent (or decentralized) decision making: removing the “approval” cap off simple, obvious decisions for cyber-physical systems, allowing them to be as autonomous as possible.
  • Sound technical assistance: assisting humans with technical aid for tasks that are either too labor-intensive or too dangerous for humans along with the general ability to assist humans with decision making and problem-solving.
  • Transparency of information: contextualizing information and datasets through systems that create virtual copies of the physical world through sensor data.
  • Heterogeneous network interactions: devices, sensors, machines, and humans connecting and communicating with one another in a complex yet seemingly manageable environment

The concerns with the major shift in Industry 4.0

If your organizational infrastructure has woven through the above elements, you are already riding the Industry 4.0 wave. But like it has happened every time a new technology has set in, a set of concerns follow the benefits.

First off, the natural extension of allowing new systems entry into existing data banks brings the threat of security breaches. The more access you give to these systems, the more data security issues you will have to deal with. Plus, proprietary production knowledge presents an adjacent concern related to IT security.

The degree of stability and system reliance required for successful cyber-physical bonding will be very high. Such states can be difficult to obtain as well as maintain. Additionally, we have the challenge of maintaining the integrity of manufacturing and production processes with dwindling human control.

Perhaps the most crucial challenge in all of this will be the loss of high-paying human jobs. We simply cannot brush under the carpet the corporate accountability for humans in a world of bots. Finally, there’s always the risk of running into expensive production outages arising out of technical inconsistencies.

First-time blues matter less than the expansive potential

The singular most challenging part of implementing new technology across industries is the lack of experience. The systemic shortage of manpower in implementing these new changes would be augmented with the general reluctance of stakeholders in trusting new technologies upfront. Low trust naturally translates into lower investments.

Irrespective of the initial round of suspicion, there’s a lot of benefits offered by the 4.0 model. More importantly, the benefits outlive the concerns by a long shot. Safety of human workers in dangerous work environments can be dramatically improved. We will exercise far greater control over the supply chains when there is processed data at every level of the delivery process.

One thing is certain. The cyber-physical combination kind of guarantees increased productivity and faster deliveries. With that, the revenue, market share, and profits – all shoot up.

Industry 4.0 demonstration city

Mayor John Carnely of Cincinnati, Ohio has declared that Cincinnati, Ohio will be an Industry 4.0 demonstration city. The primary objective of the proclamation is to create a hub for investment and manufacturing within the Industry 4.0 environment. The resolution also thanked the contributions of Prof. Jay Lee and the Center for Intelligence Maintenance Systems for their positive contributions to the global revolutions surrounding Industry 4.0. The Cincinnati move is not just strategic but also historical given the potential leverage it provides to the manufacturing hub in the city.

Besides the Cincinnati move, reports suggest that emerging economies like India will reap great benefits of Industry 4.0 practices, largely because of readily available resources and the willingness to participate in new technologies.

Are we ready for the transformation?

To be honest, most industries will follow conventional practices unless there’s a standout example of blazing success in the Industry 4.0 model. But I am afraid much time will be lost with respect to realizing the largest gains promised by the revolution. Companies that show the courage to dive in fast will well and truly emerge as the frontrunners in maximizing profits.

There are additional issues that play beyond the willingness to adapt to change. Legacy systems cannot be overhauled in a night. The efficacy of tenured systems with proprietary applications should also continue to be just as important as they are today.

The analysis of big data and dark data will be one of the first challenges that the 4.0 architecture will need to address. We presently have immense volumes of data generated by digital systems, existing sensors, and other existing equipment. Much of these data banks that are presently unaccounted for will influence decision making in the industry.

Robotic Process Automation and Industry 4.0

That we are generating more volumes of global data than ever is one side of the story. ERP systems, bots, sensors, and even cookies are collecting pools of data that is currently unused. Most manufacturers are concerned about making this connection between machines from the production unit and back office systems. Finding a way to channel the unstructured data requires more than conventional computing. It requires software-based intelligent automation.

The best bet about RPA is that it seamlessly gels in with the existing IT setup inclusive of hardware and legacy applications. Large volumes of enterprise data can be indexed and structured using RPA. When clubbed with AI, RPA presents learning capabilities with advanced data models to organize and classify all the information, thereby making it valuable. On the whole, the platform visualizes these insights and eventually helps in predicting the future.

Notwithstanding the magnanimity of the promise presented by the Industry 4.0 model, the realization of success can only come with the right RPA solutions and resources. Futran Solutions specializes in delivering composite RPA solutions and resources. To expand upon a wholesome list of RPA offerings, Futran recently partnered with Automation Anywhere – a leading platform for implementation of automation solutions across the industry.

Jyoti Vazirani is the co-founder and CEO of Futran Solutions. She is a certified SAFe Agile coach and an out and out machine learning enthusiast.

How to Draw Competitive Advantage in Business with the Power of Artificial Intelligence

Artificial Intelligence or AI is the theory and subsequent development and integration of human-like intelligent behavior in machines. Machine learning, a subset of AI finds key applications in a wide range of technologies. While machine learning is a simple way of achieving AI (say facial recognition), AI itself is a much more complex game. Businesses are using various facets of artificial intelligence to automate processes – saving both time and money.

Beyond automating processes, AI is also vastly used to solve complex problems. This is generally done by building a range of computer programs capable of automating tasks.

A virtual agent is a program that offers a conversational and cognitive user experience mostly through an animated character. More and more companies are using intelligent virtual agents as their customer service representatives. Moreover, virtual agents can spot data particular patterns that are very difficult to trace for humans.

The most overwhelming facet of both artificial intelligence and machine learning is their ability to automate. So employees can skip the manual and time-consuming routine to focus on higher value and innovative work. Improved use of Artificial Intelligence can also potentially extract newer insights, significantly impact decision making, and drive better business results.

Organizations with foresight have gone ahead with early adoption of artificial intelligence with the help of clearly defined applications. Some of these organizations have already derived enhanced business value and are now in the pursuit of sustainable transformation of business processes.

Businesses can use artificial intelligence to:

  • Increase competitive advantage and improve efficiency
  • Engage in advanced and automated interactions with clients and employees
  • Multiply productivity by automating processes and powering smarter machinery
  • Enhance customer intimacy and increase consumer demands
  • Improve real-time audio and video analysis
  • Amplify applications in industries like healthcare and automobile
  • Detect fraud by comparing millions of transactions and distinguishing between legit and fraudulent transactions

Artificial Intelligence and Finance

The BFSI industry is also using artificial intelligence extensively to manage a vast amount of data and associated discrepancies. Even other financial institutions are now increasingly dependent on machine learning for portfolio management, and customer interaction.

AI is also proving instrumental in the prediction and management of assets and wealth. Specially programmed algorithms are better at discovering a shift in trends. This enables institutions to react to situations within a split second.

AI and Health

While not quite at the pace of most other industries, healthcare organizations are adopting artificial intelligence rapidly. Complex medical data can be broken down into human cognitive levels with the help of algorithms. Furthermore, surgeries assisted by robots are not just more successful but also inflict lesser scars, facilitating quicker recoveries.

Smartwatches and other wearable tech for personalized health monitoring have infused more realism in health telemetry than we could imagine.  The US government is set to invest more than $1B in AI-powered medical initiatives like Cancer Breakthroughs 2020 and All of Us.

The Expanding Realm of Artificial Intelligence

To make the most out of artificial intelligence, it is important to link planned opportunities with problem-solving AI initiatives. Here are some pointers that will help you integrate AI into your business:

  • Start by identifying the problem and describing where you can use AI to improve efficiency
  • Identify the data source and collect the data from the relevant targets
  • Develop an AI-based solution to aid algorithmic decision making
  • Implement solutions and provide necessary training

As we slowly transition into a new decade, we will see a lot more of AI. It will be imperative for almost every industry to transform their processes. New business models will naturally become AI compliant. The bulk of the onus will stay with the management, imagination, and implementation.

Now more than ever, business leaders will need to come forward and make plans for AI to work well in their organizations. Using AI will make sure projects bear good results.

Futran Solutions provides knowledge sharing and trains AI resources through different numeric systems, pictures, and content that completely sums up machine learning for aspiring techies.