Addressing Cybersecurity in the Enterprise with AI

Cybersecurity-related risk remains one of the top sources of risk in the enterprise. This has been exacerbated by the global pandemic, which has forced companies to accelerate digitization initiatives to better support a remote workforce.

This includes not only the infrastructure to support a distributed workforce but also automation through robotics, data analytics, and new applications. Unfortunately, this expansive digital footprint has led to an increase in cybercriminal attacks.

If you are considering a new cybersecurity solution for your business, it is important to understand how traditional prevention methods differ from modern AI solutions.

Are traditional cybersecurity methods still feasible for enterprises?

The proliferation of endpoints in today’s more distributed environments makes traditional cybersecurity methods, which create perimeters to secure the infrastructure, much less effective. In fact, it’s estimated that for at least half of all attacks, the intruder is already inside.

Manual data collection and analysis process

Implementing rules-based tools or supervised machine-learning systems to combat cyberattacks is ineffective. The number of logs collected on devices and added to networks continues to increase and can overwhelm traditional collection mechanisms. Petabytes of data are easily amassed and must be sent back to a central data lake for processing.

Due to bandwidth limitations, only a small sample is typically analyzed. This might be as little as five percent of the data, so one in every 2000 packets can be analyzed. This is a suboptimal way of analyzing data for cybersecurity threats.

Most enterprises have the means to look at only a small percentage of their data. This means they are likely missing valuable data points that could help identify vulnerabilities and prevent threats. Analysts may look to enrich their view of what is happening in and around the network by integrating tools and data, but this is often a manual process. 

Lack of AI capabilities leads to longer threat detection times

It is estimated that it can take up to 277 days to identify and contain a security breach. Being able to quickly triage and iterate on a perceived threat is crucial, but also typically requires human intervention. These problems are magnified by the global shortage of cybersecurity professionals. 

Supervised ML systems also can’t detect zero-day threats because that is a “look back” cybersecurity approach. Traditional software-driven approaches like these can impede security teams from responding quickly to cybercriminals.

A better way to address threat detection challenges is with AI technology. For example, a bank institution may implement an AI cybersecurity solution to automatically identify which customer transactions are typical and which are potential threats.

How is AI changing modern cybersecurity solutions?

It’s no secret that cybersecurity professionals face an uphill battle to keep their organizations secure. Traditional threat detection methods are costly, reactive, and leave large gaps in security coverage, particularly in operations and globally distributed organizations.

To meet today’s cyberthreats, organizations need solutions that can provide visibility into 100% of the available data to identify malicious activity, along with insights to assist cybersecurity analysts in responding to threats.

AI cybersecurity use cases include:

  • Analyst augmentation technology using predictive analytics to assist with querying for large datasets.
  • User behavior risk scoring using AI algorithms to mine network data to identify and stop potential threats.
  • Reducing the time required to detect threats through faster, automated AI model updates.

Adopt an enterprise AI cybersecurity framework

NVIDIA Morpheus enables enterprises to observe all their data and apply AI inferencing and real-time monitoring of every server and packet across the entire network, at a scale previously impossible to achieve. 

The Morpheus pipeline, combined with the NVIDIA accelerated computing platform, enables the analysis of cybersecurity data orders of magnitude faster than traditional solutions that use CPU-only servers. 

Additionally, the Morpheus prebuilt use cases enable simplified augmentation of existing security infrastructure:

  • Digital fingerprinting uses unsupervised AI and time series modeling to create micro-targeted models for every user account and machine account combination running on the network, detecting humans posing as machines and machines as humans.
  • Phishing detection analyzes the entire raw email to classify it into ham, spam, or phishing.
  • Sensitive information detection finds and classifies leaked credentials, keys, passwords, credit card numbers, financial account information, and more. 
  • Crypto-mining detection addresses the issue, reported by more than 69% of enterprises, of crypto-mining malware resulting in malicious DNS traffic and over-utilization of compute resources. This model determines crypto-mining, malware, machine learning and deep learning workloads, and more.

For more information, see the full list of NVIDIA Morpheus use cases.

Next steps

To get started with Morpheus, see the nvidia/morpheus GitHub repo.

To learn about how Morpheus can help companies leverage AI to improve their cybersecurity posture, register for the free online Morpheus DLI course or check out the following on-demand GTC sessions:

For live sessions, join us at GTC, Sept 19 – 22, to explore the next technology and research across AI, data science, cybersecurity, and more.

  • Learn About the Latest Developments with AI-Powered Cybersecurity [A41142]: Learn about the latest innovations available with NVIDIA Morpheus, being introduced in the Fall 2022 release, and find out how today’s security analysts are using Morpheus in their everyday investigations and workflows. – Bartley Richardson, Director of Cybersecurity Engineering, NVIDIA.
  • Deriving Cyber Resilience from the Data Supply Chain [A41145]: Hear how NVIDIA tackles these challenges through the application of zero-trust architectures in combination with AI and data analytics, combating our joint adversaries with a data-first response with the application of DPU, GPU, and AI SDKs and tools. Learn where the promise of cyber-AI is working in application. – Daniel Rohrer, Vice President of software Product Security, NVIDIA.
  • Accelerating the Next Generation of Cybersecurity Research [A41120]: Discover how to apply prebuilt models for digital fingerprinting to analyze behavior of every user and machine, analyze raw emails to automatically detect phishing, find and classify leaked credentials and sensitive information, profile behaviors to detect malicious code and behavior, and leverage graph neural networks to identify fraud. – Killian Sexsmith, Senior Developer Relations Manager, NVIDIA.
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