Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Artificial Intelligence is everywhere nowadays. It helps teams to be more productive, but at the same time, it can threaten your critical project management data. The introduction of AI into Jira opened up new paths for attackers to exploit, new vulnerabilities coming up internally, and human errors. So, in this article, let’s speak about AI data loss in Jira and what measures to take to protect your sensitive data in Jira Cloud.

Companies that need to comply with CMMC to earn their governmental contracts have a lot of work ahead of them. Securing their systems against intrusion and protecting data from breaches, malicious actors, and snooping is all part and parcel of the program. One aspect of information security that can be distressingly easy to overlook is disposal.

Your organization just won a federal contract. During the celebratory pizza party, the CXO cust in, “though the data is not confidential, it is controlled.” Simply put, this means you’re handling sensitive government data (also known as Controlled Unclassified Information or CUI), which requires safeguarding or dissemination controls for critical information that falls just short of classified status, triggering compliance with NIST SP 800-171.

When it comes to cybersecurity, even “routine” actions like applying patches can introduce risk. An update meant to fix one vulnerability can sometimes open the door to another, disrupt operations, or create compliance headaches.

In April, I had the amazing opportunity to participate in a unique AI security event put on by the National Cybersecurity Center of Excellence (NCCoE). The April event was all about getting the community together to discuss what a Cyber AI Profile should look like as an overlay to the NIST Cybersecurity Framework (CSF) 2.0.

In this article If your cloud platform is already compliant with NIST SP 800-53, you’ve laid important groundwork for security and risk management. But when the goal shifts to serving U.S. federal agencies, the bar is raised significantly. That’s where FedRAMP enters the picture. While FedRAMP is built on NIST 800-53, the two are not interchangeable. FedRAMP adds a layer of rigor, documentation, and oversight specifically tailored to the requirements of the federal government.

The National Institute of Standards and Technology (NIST) has unveiled a new metric that promises to revolutionize the way vulnerability management is prioritized. Likely Exploited Vulnerabilities (LEV) is a ranking designed to help organizations focus their efforts on the flaws that cybercriminals are actively using to perpetrate real-world attacks. Thousands of vulnerabilities are reported every year but only a small fraction is exploited in the wild.

As artificial intelligence continues to reshape industries, responsible governance has emerged as a business necessity. Organizations deploying AI face the challenge of maintaining innovation while mitigating risks related to bias, data privacy, security, and transparency. Two major frameworks – ISO 42001 and NIST AI Risk Management Framework (AI RMF)—have been developed to help businesses navigate this balance.