How Agencies Can Gain the Cyber Edge with Smart Data Solutions
For the vast majority of US citizens, the front lines of conflict are witnessed from thousands of miles away on the nightly news. But for government agencies, these physical conflicts are the tip of the iceberg as cyberattacks persist as an underlying constant, inflicting enduring damage regardless of geopolitical tension or location.
Consider the fact that the SolarWinds Orion supply chain breach (attributed to Russia) from 2020 continues to make news, with many customers still in the dark as to whether they were compromised or not. The US Security and Exchange Commission recently announced a class action lawsuit enforcement action taking place stating that SolarWinds didn’t do enough to secure its customers. The attack targeted a host of public and private sector organizations (18,000 customers) including NASA, the Justice Department, and Homeland Security, and it is believed the attackers persisted on SolarWinds systems for 14 months prior to discovery.
The question then, at the forefront of CISOs’ minds is, “How do you outpace the threats given the current IT estate?” Or better yet, “How do we empower people with enterprise data solutions that amplify positive outcomes in the security operations center?”
The answer: You must amplify the impact of data science talent on the mission and the cyber front lines, delivering advanced analytics products powered by machine learning (ML) into the hands of the threat hunters.
The benefits and challenges of ML operations
The world is awash in data. Nearly everything around us is producing or consuming data at exponential rates, most of which is connected to the Web. The same holds true for the public sector. Data is everywhere as an opportunity and a target for malicious actors. But ML presents your cyber security operations with a force multiplier to leverage smart data solutions at scale in the cyber-mission. ML enables:
- Massive amounts of historic and real-time data to be synthesized and prepared for analysis
- Manual and repetitive tasks to be automated, freeing up resources for time-critical tasks
- Continuous learning from evolving data sets including labeled detections and alerts
- Automated predictive threat detection, evaluation, and response
- Augmentation of analyst insights with real-time ML detection
- Maximizing true positive detections while minimizing false positives
While these benefits are a potential boon for your cyber security operations, real-world implementation is often hindered by technical barriers, disparate systems, and interfaces that result in ML production swivel chair. Workflows become so cumbersome that projects never make it past pilot and most importantly, data scientists’ ML models rarely emerge from experimentation to operation.
Operationalize ML with the Cloudera Data Platform
In order to overcome the technical barriers, you need a unified data platform that consolidates the data management solutions that support ML operations into a single interface that delivers optimal performance, scalability, and security.
The Cloudera Data Platform (CDP) is a hybrid data platform for all your end-to-end machine learning (ML) production use cases. CDP enables you to collect, monitor, analyze, and store data from continuous streams, and once the data has been stored in the data lake house, CDP enables the entire ML life cycle by allowing your users to train, package, and serve ML models from a single secure platform. All with the integrated security and governance technologies required for compliance.
With CDP you can unlock the potential of your data scientists as ML models emerge from research projects to mission-critical components or services that can be productized and handed off to analysts without requiring details of the model, empowering the operator or cyber analyst while reducing the mean time for threat resolution.
To learn more, read our technical brief: Securely Train and Deploy Machine Learning Models in Production with Cloudera Data Platform.
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