Recently, a critical API vulnerability in FortiManager (CVE-2024-47575) was disclosed. Certain threat actors exploited it in the wild to steal sensitive information containing configurations, IP addresses, and credentials used by managed devices. In advanced notification emails, Fortinet warned its users of the vulnerability and mitigation steps. The vulnerability has a critical severity rating of 9.8 out of 10.

Before the introduction of static code analysis tools, securing mobile applications often felt like playing catch-up. Development teams would spend months building features, only to discover critical vulnerabilities late in the release cycle. This last-minute scramble to fix security issues delayed product launches and stretched resources thin—adding more pressure on developers and security teams.

AI code generators are revolutionizing the way developers write and maintain code. These advanced tools leverage machine learning (ML) and natural language processing (NLP) to significantly boost productivity, improve code quality, and enhance security. However, they can sometimes introduce subtle vulnerabilities if not carefully monitored. With generative AI, software developers can complete coding tasks up to x2 faster.

The growing reliance on generative AI is transforming industries across the globe. From automating tasks to improving decision-making, the potential of these systems is vast. However, with this progress comes significant risks. Generative AI can be unpredictable, creating new vulnerabilities that expose organizations to data privacy breaches, compliance failures, and other security issues. So, how can companies harness the power of AI while ensuring they remain protected?

93% of last year’s data breaches began with compromised credentials. Before the cloud, security perimeters were defined by physical walls and network boundaries, but in the cloud, that perimeter has all but dissolved. Consider what happened in November 2023, when a cloud observability vendor found evidence of unauthorized access to its staging environment — an environment that housed customer data and PII.

In the wake of Cisco’s recent data breach involving exposed API tokens - amongst other sensitive information - the cybersecurity community is reminded once again of the significant risks associated with unsecured APIs. Though Cisco has asserted that the damage was limited to a public-facing environment, such breaches demand a more cautious evaluation. Exposing sensitive information like API tokens, credentials, and even source code can have broader security implications than initially apparent.