How AI-Based Cybersecurity Strengthens Business Resilience

The world's 5 billion internet users and nearly 54 billion devices generate 3.4 petabytes of data per second, according to IDC. As digitalization accelerates, enterprise IT teams are under greater pressure to identify and block incoming cyber threats to ensure business operations and services are not interrupted - and AI-based cybersecurity provides a reliable way to do so.

Few industries appear immune to cyber threats. This year alone, international hotel chains, financial institutions, Fortune 100 retailers, air traffic-control systems and the U.S. government have all reported threats and intrusions.

Whether from insider error, cybercriminals, hacktivists or other threats, risks in the cyber landscape can damage an enterprise's reputation and bottom line. A breach can paralyze operations, jeopardize proprietary and customer data, result in regulatory fines and destroy customer trust.

Using AI and accelerated computing, businesses can reduce the time and operational expenses required to detect and block cyber threats while freeing up resources to focus on core business value operations and revenue-generating activities.

Here's a look at how industries are applying AI techniques to safeguard data, enable faster threat detection and mitigate attacks to ensure the consistent delivery of service to customers and partners.

Public Sector: Protecting Physical Security, Energy Security and Citizen Services AI-powered analytics and automation tools are helping government agencies provide citizens with instant access to information and services, make data-driven decisions, model climate change, manage natural disasters, and more. But public entities managing digital tools and infrastructure face a complex cyber risk environment that includes regulatory compliance requirements, public scrutiny, large interconnected networks and the need to protect sensitive data and high-value targets.

Adversary nation-states may initiate cyberattacks to disrupt networks, steal intellectual property or swipe classified government documents. Internal misuse of digital tools and infrastructure combined with sophisticated external espionage places public organizations at high risk of data breach. Espionage actors have also been known to recruit inside help, with 16% of public administration breaches showing evidence of collusion. To protect critical infrastructure, citizen data, public records and other sensitive information, federal organizations are turning to AI.

The U.S. Department of Energy's (DOE) Office of Cybersecurity, Energy Security and Emergency Response (CESER) is tasked with strengthening the resilience of the country's energy sector by addressing emerging threats and improving energy infrastructure security. The DOE-CESER has invested more than $240 million in cybersecurity research, development and demonstration projects since 2010.

In one project, the department developed a tool that uses AI to automate and optimize security vulnerability and patch management in energy delivery systems. Another project for artificial diversity and defense security uses software-defined networks to enhance the situational awareness of energy delivery systems, helping ensure uninterrupted flows of energy.

The Defense Advanced Research Projects Agency (DARPA), which is charged with researching and investing in breakthrough technologies for national security, is using machine learning and AI in several areas. The DARPA CASTLE program trains AI to defend against advanced, persistent cyber threats. As part of the effort, researchers intend to accelerate cybersecurity assessments with approaches that are automated, repeatable and measurable. The DARPA GARD program builds platforms, libraries, datasets and training materials to help developers build AI models that are resistant to deception and adversarial attacks.

To keep up with an evolving threat landscape and ensure physical security, energy security and data security, public organizations must continue integrating AI to achieve a dynamic, proactive and far-reaching cyber defense posture.

Financial Services: Securing Digital Transactions, Payments and Portfolios Banks, asset managers, insurers and other financial service organizations are using AI and machine learning to deliver superior performance in fraud detection, portfolio management, algorithmic trading and self-service banking.

With constant digital transactions, payments, loans and investment trades, financial service institutions manage some of the largest, most complex and most sensitive datasets of any industry. Behind only the healthcare industry, these organizations suffer the second highest cost of a data breach, at nearly $6 million per incident. This cost grows if regulators issue fines or if recovery includes legal fees and lawsuit settlements. Worse still, lost business may never be recovered if trust can't be repaired.

Banks and financial institutions use AI to improve insider threat detection, detect phishing and ransomware, and keep sensitive information safe.

FinSec Innovation Lab, a joint venture by Mastercard and Enel X, is using AI to help its customers defend against ransomware. Prior to working with FinSec, one card-processing customer suffered a LockBit ransomware attack in which 200 company servers were infected in just 1.5 hours. The company was forced to shut down servers and suspend operations, resulting in an estimated $7 million in lost business.

FinSec replicated this attack in its lab but deployed the NVIDIA Morpheus cybersecurity framework, NVIDIA DOCA software framework for intrusion detection and NVIDIA BlueField DPU computing clusters. With this mix of AI and accelerated computing, FinSec was able to detect the ransomware attack in less than 12 seconds, quickly isolate virtual machines and recover 80% of the data on infected servers. This type

LINK:	https://blogs.nvidia.com/blog/2023/11/03/ai-cybersecurity-business-res...
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