Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

NIST 800-171 revision 3 was released on May 14, 2024, prompting DoD to issue an indefinite class deviation for DFARS 252.204-7012, Safeguarding Covered Defense Information and Cyber Incident Reporting (DFARS 7012). US Defense Industrial Base (DIB) contractors must now comply with NIST SP 800-171 revision 2 rather than the version in effect at the time the solicitation is issued, as was previously required.

AI is everywhere—but how can you be sure that your data isn’t being used to train the AI models that power your favorite SaaS apps like Slack? This topic reached a fever pitch on Hacker News last week, when a flurry of Slack users vented their frustrations about the messaging app’s obtuse privacy policy. The main issue?

Cybercriminals gather personal information about their targets by using social engineering techniques, looking at social media accounts and collecting data that gets leaked from public data breaches. The more personal information a cybercriminal can collect about their target, the easier it is for them to launch cyber attacks that their targets will easily fall for. Continue reading to learn more about how cybercriminals gather their target’s personal information and how you can keep your data safe.

When we think about storing sensitive information, two types of information come to mind: digital data and physical data. While physical data can be securely stored in a physical vault, digital data should be stored in an encrypted cloud storage solution to protect it from common cyber threats. Continue reading to learn the best ways to store your sensitive physical and digital information, plus the storage methods to avoid.

After the debut of ChatGPT, and the ensuing popularity of AI, many organizations are leveraging large language models (LLMs) to develop new AI-powered apps. Amidst this exciting wave of innovation, it’s essential for security teams, product managers, and developers to ensure that sensitive data doesn’t make its way into these apps during the model-building phase.

Everywhere we look, organizations are harnessing the power of large language models (LLMs) to develop cutting-edge AI applications like chatbots, virtual assistants, and more. Yet even amidst the fast pace of innovation, it’s crucial for security teams and developers to take a moment to ensure that proper safeguards are in place to protect company and customer data.