Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

On June 5, 2026, ServiceNow quietly pushed a security update to hosted customer instances. The fix, described in an internal knowledge base article, addressed a flaw that let unauthenticated users gain more access to ServiceNow-hosted data than they were ever supposed to have. No password. No credentials. The remediation itself tells the whole story: ServiceNow changed an endpoint configuration to restrict access to authenticated users only. Read that again.

America's cybersecurity agency left its production credentials sitting in a public GitHub repo for six months. The same failure pattern is now being automated by AI agents in every enterprise running Cursor, Claude Desktop, or Copilot.

On May 14, GitGuardian found a public GitHub repository called "Private-CISA" — 844 MB of plain-text passwords, AWS tokens, and Entra ID SAML certificates belonging to CISA, exposed since November 2025. Some credentials were still valid. CISA pulled it offline within 26 hours.

Data leaks are no longer rare incidents. They have become a constant concern for organizations of all sizes. A single exposed file can lead to compliance violations, financial penalties, and long-term damage to brand reputation. In many cases, the impact builds over time as sensitive data spreads beyond control. At the same time, the nature of data has changed. Important information is no longer limited to structured formats like databases or spreadsheets.

Data is a critical asset for modern businesses, so keeping it safe becomes extremely important. Cybercriminals use sophisticated methods to steal data, but these threats can be mitigated through Data Loss Prevention (DLP). It is a security framework that includes policies and tools to protect data from attackers and unauthorized access. Many organizations use DLP to protect confidential data from external threats and accidental exposure. Organizations that lack a strong DLP plan risk exposing their data.

Sensitive information disclosure ranks on the OWASP Top 10 for LLM Applications, and for good reason. When AI-powered applications inadvertently expose private data like personally identifiable information (PII), financial records, health information, API keys, or proprietary business intelligence, the consequences cascade quickly: regulatory violations, competitive disadvantage, and shattered user trust.

Earlier this year, twin brothers Muneeb and Sohaib Akhter, both government contractors, were fired from their employer. Minutes later, they began a weeklong insider attack that compromised or destroyed data belonging to more than 45 federal agencies.

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.

Website data leaks don’t require hackers. They happen when legitimate scripts, analytics pixels, and chat widgets transmit sensitive data to third parties through routine operations. Traditional security tools miss these leaks because they monitor server-side traffic while the exposure occurs in customer browsers. This visibility gap is why organizations use client-side monitoring platforms to detect browser-level data flows that security tools can’t see.