Augment your Cybersecurity Journey with Data Lakes & AI
We have reached an extraordinary stage in the development of technology and its application in solving business problems. In fact, we have reached an inflection point, where the systems that we currently use have become so complex that we begin to experience the law of unintended consequences. Any action performed in one part of a major system may have unintended effects on other parts. In the coming years, we will witness the impact of complexity in the cybersecurity environment as well.
Taking supply chain security as an example, we have data flowing between suppliers, manufacturers, distributors, retailers and consumers, driving business decisions. When the proportion of algorithmic and automated decisions increase, the data flow becomes even more crucial. Secondly, when various parties in the supply chain have a vested security intent, the distributed data ownership and the value of that data can make security collaboration difficult. The resulting gaps in the security islands represent a challenge. As cross-organization practices become increasingly automated, data integrity becomes critical.
In a digital business context, this is not about risk management but business agility. It must be supported by intelligent security architecture, where policies help us to start (based on what we are predicting) and drive preventive processes. Further, we need to have detection and response capabilities. Providing a feed of information at every step of the way lets an organization have visibility and drive intelligence to make informed decisions.
- Data Lakes: It is not enough to have static information. We need a continuous, event-driven kind of mode associated with the information. To manage this complexity, we must create a secure environment and deliver differently (preventing, detecting, responding and predicting). This will create vast number of data lakes.
- Cloud: As organizations move to the cloud, we need to shift the focus from protecting infrastructure to protecting data. In the world of digital business, our sensitive data is everywhere and we must know how to control it.
- Data Classification: Data classification is a fundamental need for data compliance. It brings focus and efficiency for data security and governance. We need to have a risk-based approach for data classification and focus on the initial implementation of datasets for high-risk scenarios.
- AI: AI and all its associated sub-environments are applied around analytics as the next step for an algorithmic model. The context from internal and external data sources facilitate the move towards continuous awareness, to provide a better way of security machine learning and create a more accurate and granular view of what is happening.
Andrew serves the Resources & Services team and applies his knowledge to spend time with companies that are at the forefront of digital disruption, focused on artificial intelligence, automation, privacy.