Sathish Murthy, Senior Systems Engineering Lead, Cohesity ASEAN & India

Organizations that prioritize the collection, storage, and analysis of high-quality data are better positioned to harness AI’s potential and gain a competitive advantage. Yet, in today’s complex, distributed architecture, organizations face challenges in managing their data across and throughout their diverse data estates, which often consist of hybrid or multi-cloud data stores. Therefore, organizations should prioritize:

  • Data Aggregation and Unification: Organizations need to simply the process of gathering and consolidating data from all sources to establish a unified data repository that includes all their data stored on-premises, cloud, and at the edge. By dismantling data silos and offering a holistic view of organizational data, this supports the AI tools in detecting patterns, trends, and anomalies. Using backup data that is indexed and aggregated allows organization to leverage AI in a highly effective manner that doesn’t consume too much storage space or compute power on production systems and doesn’t risk direct exposure of their production systems to AI applications.
  • Data Optimization: Enterprises are storing more data every day and in many cases this is because of data duplicates. These redundancies can occupy large amounts of storage that require huge investments – not just on the storage side, but also in the overall IT infrastructure such as networking, archival, cloud bandwidth, and manageability. When businesses organize their data sets into more compact structures, this creates a more robust AI tool that can identify trends and patterns more efficiently. With infrastructure spanning private data centers, clouds, and edge locations, organizations that invest in collecting, storing, and analyzing high-quality data will be better positioned to leverage the power of AI.

Enterprises stand to gain significantly from adopting cutting-edge data security, management, and recovery technologies, particularly with the integration of AI and ML in capabilities such as:

  • AI-Enabled Multifactor Access (MFA): MFA adds an extra layer of security to ensure that only authorized users access sensitive information. By integrating AI into MFA, organizations can enhance authentication mechanisms based on data risk levels and implement fraud detection systems that automatically block users displaying abnormal access behavior.
  • AI Retirement of Inactive Data: Leveraging AI, organizations can identify dormant data suitable for archiving, streamlining recovery processes by eliminating unnecessary retrieval of unused data. This approach not only enhances efficiency but also reduces storage costs.
  • AI & ML-Powered Anomaly Detection: AI AI and ML-powered anomaly detection systems alert IT teams to unplanned or abnormal changes in data’s size or format, which is often indicative of malicious activity. By detecting anomalies early, organizations can mitigate potential threats before they escalate and at a minimum have a ‘clean point’ or indication of how to remediate and respond to a cyber or data security incident.