ManageEngine Vice President Rajesh Ganesan shares how new applications with established technologies will help organizations to work smarter in dealing with privacy law compliance, security and cost management strategies.
By Rajesh Ganesan
This year, we will see countries following the European Union’s lead by implementing data protection laws similar to GDPR, such as the Thailand Personal Data Protection Act (PDPA) which goes into effect in May 2020.
Under such scenarios, the role of Data Protection Officers assumes significance as they must work closely with the CIOs and tech teams to ensure that organizations comply with the law.
As awareness of and emphasis on data protection increases, there will be an even greater focus on the handling of users’ personal data and its security. Employees at all levels will be held accountable as organizations strive to meet compliance. Therefore, there will be a need for upskilling and education programs to handle this aspect.
On the AI front, even as its adoption across enterprises is set for continued growth, organizations will realize the importance of securing systems. At least three aspects need attention to make AI work optimally. The first is to ensure the attackers do not mislead the system to make it perform the way they want in real-time, for example: introducing bias. Enterprises will see value in investing in explainable AI technologies, which involve the AI system explaining its actions and decisions thus making it possible to review and correct the AI in real time.
The second is to protect the AI training data and the ML models, possibly by investing in technologies like homomorphic encryption. The third is guarding against the dangers of ‘concept drift’, which is when the AI models built using the training data become irrelevant and the system behaves erratically.
This year we will also begin to see the rise of hyper-automation, which is the meeting point of intelligence driven by AI and ML with autonomy driven by robotic and cognitive process automation.
Hyper automation will help support dynamic and complex business processes including loan processing, insurance claims, warehouse dispatch, and others. This will provide the unique advantage of mimicking user actions on terminals like carrying out transactions and generating dynamic content contextually to deliver on speed, accuracy, reliability and reduced costs.
We will also see endpoint protection becoming a top priority. As the number and types of endpoint devices rapidly proliferate and become ‘smart powered’ by software and connectivity, they become critical targets and vectors for external attacks. Mobile applications may well be the source of the next large-scale enterprise security breach, even as the influx of non-traditional workers and their remote devices continue.
As the threat landscape evolves to exploit sophisticated capabilities in the endpoints, so must the protection techniques. This year we’ll see techniques such as data loss prevention, which prevents users from leaking critical information intentionally or otherwise, and endpoint detection and response, which continuously monitors events, detects threats, performs investigations, and initiates responses.
These techniques will gain prominence because the logic and intelligence underpinning them will increasingly reside on the devices themselves.
The democratization of data has also opened up analytics usage to departments that have traditionally not employed analytics for decision-making – such as IT. This means that there are now new and different sources of data that need to be standardized and checked for quality before they can be used for analysis.
Obtaining insights from data takes far less time when data from various sources are structured to fit a common schema or format, otherwise known as data standardization. To accommodate this, next year is going to see a rise in the demand for ETL (extract, transform, load) tools, which help cut down the time it takes to standardize data. Analysts have to begin familiarizing themselves with newer sources of data and employ ETL tools, when necessary.
Finally, 2020 will see more businesses try to contain rising cloud costs. The cloud, particularly SaaS, has democratized the use of technology across all business functions. However, it has also resulted in spiraling costs and significant waste due to the decentralized model of consumption. Surveys indicate businesses may be wasting up to 35 per cent of their cloud costs because of duplicate spending and lack of usage.
Fortunately, solutions are emerging to help the CFOs and CIOs take control of the situation. For example, a SaaS management platform (SMP) can bring central visibility, control, and manageability for all the SaaS applications used within the business, including cost management. Different services could have different pricing, costing and billing models; and SMPs can help provide cost and efficiency insights at the level of user, department, and organization.
Cloud cost management solutions may provide unified cost management for organizations that use multiple IaaS providers. For businesses struggling with managing cloud costs, these will become top priorities.