Network Security Archives - Artificial Intelligence

How to Secure Your Docker Containers: Best Practices and Tips

Maruti Kr. — Thu, 30 Nov 2023 12:13:42 +0000

Securing your Docker containers is crucial for safeguarding your applications and infrastructure from potential vulnerabilities and attacks. By implementing a comprehensive security strategy, you can minimize the risk of data breaches, unauthorized access, and other malicious activities.

Here are some best practices and tips:

Use Official and Trusted Images: It’s recommended to use official Docker images from trusted sources. These images are regularly updated, well-maintained, and are less likely to contain vulnerabilities.
Regularly Update Docker and Containers: Keep your Docker engine and containers up to date with the latest security patches. Set up a process to regularly check for updates and perform necessary upgrades.
Enable Content Trust: Docker’s Content Trust feature ensures that only signed and verified images are pulled and run on your system. Enable this feature to prevent the execution of tampered or malicious images.
Isolate Containers: Utilize Docker’s containerization features to isolate containers from each other and the host system. Use separate containers for different components of your application and restrict network access between containers.
Limit Privileges: Run containers with the least privileges required. Avoid running containers as root. Instead, run them as non-root users to reduce the potential impact of container compromises.
Implement Least Privilege Principle: Limit container capabilities to only what is necessary for their intended function. Disable any unnecessary host system capabilities inside the container.
Control Resource Usage: Set resource limits for containers to prevent resource hogging or denial of service attacks. Define limits for CPU, memory, and other resources based on your application’s requirements.
Monitor and Audit: Implement container logging and monitoring to identify any unusual or suspicious activities. Regularly review logs and audit container activities

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Challenges of Artificial Intelligence Adoption in Healthcare

aiuniverse — Sat, 15 Feb 2020 06:33:51 +0000

Source: hitinfrastructure.com

February 14, 2020 – Artificial Intelligence (AI) adoption is gradually becoming more prominent in health systems, but 75 percent of healthcare insiders are concerned that AI could threaten the security and privacy of patient data, according to a recent survey from KPMG.

Although 91 percent of healthcare respondents believe that AI implementation is increasing patient access to care, the survey of 751 US business decision makers uncovered. The survey explored the barriers and challenges that have the potential to hamper the integration of AI technologies in healthcare organizations.

Healthcare security is a top concern for insiders with 75 percent responding that they believe AI could threaten patient data privacy. But 86 percent of respondents said their organizations are taking steps to protect patient privacy as it implements AI.

Organizations believe that a broad understanding of AI and talent in the space are musts to ensure success, but many insiders reported major challenges in these areas.

Despite this, only 47 percent of healthcare insiders responded that their organizations offer AI training courses to employees. While only 67 percent said their employees support AI adoption, the lowest ranking of any industry.

“Comprehending the full range of AI technology, and how best to apply it in a healthcare setting, is a learned skill that grows out of pilots and tests. Building an AI-ready workforce requires a wholesale change in the approach to training and how to acquire talent. Having people who understand how AI can solve big, complex problems is critical,” Melissa Edwards, managing director and digital enablement at KPMG said in the survey.

Cost is a major barrier for organizations as well. Successful AI implementation requires a large investment, which means that organizations who are already feeling budget-burned may be slower to fund AI.

Thirty-seven percent of healthcare industry executives reported that the pace in which they are implementing AI is too slow.

But Edwards highlighted that the pace has actually greatly increased in the past few years.

“The pace with which hospital systems have adopted AI and automation programs has dramatically increased since 2017,” she said.” Virtually, all major healthcare providers are moving ahead with pilots or programs in these areas. The medical literature is showing support of AI’s power as a tool to help clinicians.”

Fifty-four percent of executives voiced that to date, AI has increased the overall cost of healthcare. “The question is, ‘Where do I put my AI efforts to get the greatest gain for the business? Trying to assess what ROI will look like is a very relevant point as they embark on their AI journey,” Edward said.

Last year, The White House called for more transparency and “explainability” in healthcare AI through the National Artificial Intelligence Research and Development Strategic Plan: 2019 Update.

The plan identified eight strategic priorities for federally-funded AI research including to prioritize investments in the next generation of AI that will drive discovery and insight and enable the US to remain a leader in AI and develop effective methods for human-AI collaboration.

The plan also included:

Addressing the ethical, legal, and societal implications of AI
Ensuring the safety and security of AI systems
Developing shared public datasets and environments for AI training and testing
Evaluating AI technologies using standards and benchmarks
Understanding the national AI R&D workforce needs
Expanding public-private partnerships to accelerate advances in AI

“AI technologies are critical for addressing a range of long-term challenges, such as constructing advanced healthcare systems, a robust intelligent transportation system, and resilient energy and telecommunication networks,” the plan concluded.

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What are the pros and cons of machine learning in network security?

aiuniverse — Wed, 13 Feb 2019 06:22:08 +0000

Source- searchnetworking.techtarget.com

Machine learning has become a valuable component of network security products, but technology is a mixed blessing. Unfortunately, hackers can also employ machine learning.

Machine learning in network security separates legitimate network and application operations from attacks by building a set of rules that characterize both activities. Meanwhile, attackers can use machine learning to probe network defenses, identify ways to evade rules and carry out successful attacks.

Pros and cons of machine learning in network security

One advantage of machine learning in network security is that it can identify a zero-day attack. It takes time to identify and analyze a new signature-based attack, but machine learning can apply rules that differentiate legitimate operations from attacks. A new form of malware can be detected based on its actions, so previous observation and analysis are unnecessary.

Organizations can prepare machine learning software for operation in several ways. The software can be presented with a set of inputs labeled as attacks and other inputs labeled as legitimate. The software can then learn by analyzing a set of characteristics that differentiate between the attacks and the legitimate activity.

Spam filtering is a simple example. With spam filtering, admins create a list of words that typically show up in spam. Another approach is to supply a set of emails with some identified as spam and some as legitimate. The software can then develop a set of rules based on words that showed up in each email category. As it operates, the software continues to extend its rules based on other words and email addresses often associated with spam.

Machine learning software can also build a set of rules based on observed network and application operations. For example, the software can monitor a transaction-based application and recognize how it accesses a critical database. Actions that do not follow the same pattern, such as multiple attempts to access a database, would be blocked.

Unfortunately, academic and corporate research on techniques for machine learning in network security cannot be hidden from attackers. Attackers have begun to use the technology to repetitively probe network defenses to determine the rules that antimalware software uses to identify attacks.

Typically, attackers gain value from a successful attack, so they will undoubtedly continue to develop ways to deceive defenses. As they do, researchers will need to continually improve defenses.

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