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.

Topics discussed in this piece: click to expand

Benefits of using AI in Jira 

AI in Jira brings several benefits to its users. Let’s begin with some statistics to outline the general theme of AI in DevOps:

  • 92% of Indian professionals agree that AI will improve the speed and quality of their team’s work.
  • Globally, about 27% of the work week is wasted searching for information each year within the Fortune 500.
  • 51% of knowledge workers believe they could work faster if their teammates used AI more.

With AI in Jira, users can automate tasks, improve communication, enhance collaboration, and even integrate with tools like Slack to accelerate Jira project workflows. Key benefits of AI in Jira range from increased productivity due to less manual input and less repetitive tasks to issues getting resolved faster with quick context gathering and recommendations. Data-driven information from Jira AI analysis makes decision-making easier, and content like comments or issue descriptions can be generated from just a single prompt. Moreover, summaries give teams the ability to shorten long texts into clear overviews. Users also get a virtual agent in the form of an AI chat that is always available to answer questions. While AI in Jira Cloud boosts project management efficiency, teams must also safeguard sensitive information to avoid compliance risks.

AI compliance in Jira 

In terms of compliance, Jira admins and C-level staff should ensure that AI data protection practices are aligned with industry data security best practices and regulations. The goal is data security and to stay continuously compliant. This is especially important for teams that work with sensitive data in regulated industries. In 2024, the EU AI Act was released, and it will fully apply by 2026. This is the first comprehensive AI regulation in the world. The Act has a risk-based system to segregate issues based on different obligations depending on the potential impact of AI systems on security.

Unacceptable risk – social scoring, real-time biometric surveillance, and manipulative AI targeting children. All are banned in the EU.

High-risk AI that impacts critical infrastructure (healthcare, law enforcement, education, and employment) must undergo strict assessments, be registered in an EU database, and complete all relevant compliance checks.

Generative AI (models like GPT-4 or 5) must be transparent, the content must be labeled as AI-generated, copyrighted training data disclosed, and protections built against illegal or harmful outputs.

AI Bill of Rights

The White House Office of Science and Technology Policy introduced the Blueprint for an AI Bill of Rights. It shows the five principles to protect Americans from the potential harms of automated systems. Unlike the EU AI Act, it is not a law that must be enforced but a framework to guide the overall use of AI.

The core five principles are:

  • Safe and Effective Systems
  • Algorithmic Discrimination Protections 
  • Data Privacy
  • Notice and Explanation 
  • Human Alternatives, Consideration, and Fallback 

National Institute of Standards and Technology (NIST)

In January 2023, the U.S. National Institute of Standards and Technology (NIST) rolled out the AI Risk Management Framework (AI RMF). This was to help many organizations identify and manage risks related to artificial intelligence. However, unlike the EU AI Act, the AI RMF is voluntary guidance, but it is still widely recognized as a best practice for building trustworthy and reliable AI systems or tools.

When configuring Jira Cloud, admins should start with the basic settings and enforce strict access control – these are key points of any data protection strategy. Now, Jira admins and decision makers shall take AI RMF into account as it provides practical steps to assess risks of using AI features in ecosystems like Jira Cloud. Themes range from preventing AI bias and data leakage to guaranteeing accountability if any AI impacts a workflow or decision-making processes to protect data consistently. Admins can strengthen compliance by applying data classification to Jira issues, using custom data detectors to flag sensitive content types before they spread across projects.

Reasons for data loss in Jira because of AI 

Now, let’s take a look at risks and how AI can be exploited in a Jira environment. Though Jira is a tool that increases productivity and helps to manage projects, the introduction of AI has brought new risks that must be addressed with data loss prevention solutions.

  • First, prompt-injection attacks; these are basically malicious or accidental instructions embedded in issue text that can steer AI assistants (summaries, chat, automations) to reveal or even alter sensitive data. Without proper detection rules and policies, AI-driven automations may unintentionally expose fields and fail to protect data, leading to data exposure across projects.
  • Next, AI-generated summaries, ticket resolutions, or generated comments may also touch on sensitive data and could be factually wrong. Generative AI summaries or resolutions that are wrong can overwrite fields, close issues, or trigger wrong fixes at scale (this is especially true if paired with automation in Jira). Even NIST’s GenAI Profile flags confabulation and information-integrity failures as some of the main risks. Teams should enforce data loss prevention (DLP) controls with clear detection rules to detect and stop inaccurate or manipulated AI outputs from spreading.
  • Regulated industries (finance, healthcare, government) may be an issue since AI can violate compliance requirements if any of the features store or process regulated sensitive data outside approved regions. This, for instance, can be sensitive data like financial information.

Atlassian’s MCP protocol is used to embed AI into Jira workflows. This may possibly be exploited using the aforementioned prompt injection attacks on Jira. Usually, external users have access controls limiting permissions to data. For example, they shouldn’t be able to edit workflows or see sensitive data. Internal users, however, have increased access permissions. This means they can see the confidential information and change issues around.

The danger may occur when the Jira user starts an AI action like summarizing a ticket, the AI runs it via MCP as if the user themselves did it. So, the AI receives the same level of access control as the user who runs it. Therefore, an attacker can use a malicious prompt to trick the AI into pulling sensitive information, rewriting it into the ticket, and generally performing actions that any external individual should and would not ever be able to do on their own. Because AI assistants inherit user permissions, a malicious prompt can expose sensitive data or alter sensitive information, which is why organizations must implement detailed policies to safeguard sensitive information at every stage of the workflow. In a Jira Cloud environment, implementing DLP for Jira helps reduce the chance of data leakage by monitoring sensitive workflows and controlling how information is shared across projects. Check out this CISO report for more information regarding Atlassian security events.

Conclusion 

To sum up, Jira users do benefit from the introduction of AI. There is less manual work, decision-making is accelerated, and users can take advantage of natural language search as well as chat assistance. When leveraging the AI implemented into Jira, it is important to educate all users, stakeholders, and employees regarding data security practices implemented and the safe use of all AI functionalities, along with potential violations.

Jira Cloud admins should implement data protection measures, including data loss prevention (DLP) policies, to help protect sensitive data across projects. Introduce monitoring to see any suspicious content in tickets or comments and treat AI-generated text (summaries or recommendations), as drafts or suggestions, but not your final version – make sure you validate what the AI generates.

It is important to track the flow of sensitive data in Jira environments to identify and address risks. Stick to the principles of least-privilege permissions, since AI gets the same permissions as the user who triggers them, this is important to limit the potential blast radius of any exploits. Make sure that you follow relevant compliance frameworks and securely integrate Jira with any of the external apps, while carefully limiting how these apps can contact or access your instance. Advanced DLP for Jira solutions can configure granular detection rules to handle various content types. Among the key features are context and detail-rich notifications, which help admins respond quickly. Strong solutions also provide granular control over policies and may include automated remediation options to minimize human error in Jira.

Last but not least, opt for Jira third-party backup and disaster recovery software, as this is your data loss prevention that allows you to restore data quickly. Minimize any Jira downtime, stay away from legal issues, and keep your business operations going.

Before you go:

📚 Learn more about AI in Atlassian tools and its benefits.
🔎 Check out if your Jira data is protected with our Jira data protection assessment guide.
💡 Find out which backup best practices can improve your data security strategy in Jira.
📅 Book a demo to discover how backup & DR software for Jira can help you mitigate the risks.
🚀 Try GitProtect backup and Disaster Recovery software for Jira and learn what a compliant Jira backup strategy is 

Miłosz Jesis, Technical Content Writer at GitProtect.io
Milosz is Technical Content Writer at GitProtect, demonstrating fluency in both Polish and English, and a passion for language and technology. Currently pursuing a degree in Philosophy at UWE Bristol, he excels in creating engaging technical content that bridges the gap between users and the emerging technologies. Milosz leverages his writing skills and technical knowledge to author articles and blog posts, with a focus on DevOps, cyber-security, and potential cyber-threats, among other crucial IT topics. Additionally, valuable translations provided by Milosz further enhance GitProtect's communication and global outreach.

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