Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Managing the risks of AI development tools is crucial for organizations looking to responsibly and effectively leverage this technology’s potential. AI offers transformative capabilities, particularly in coding assistance, where tools can speed up development and reduce manual workloads. However, these benefits can come with risks, such as security vulnerabilities and compliance challenges, that cannot be overlooked.

Generative AI is changing industries by making automation, creativity, and decision-making more powerful. But it also comes with security risks. AI models can be tricked into revealing information, generating harmful content, or spreading false data. To keep AI safe and trustworthy, experts use GenAI Red Teaming. This method is a structured way to test AI systems for weaknesses before they cause harm.

Every day, security teams are expected to manage risks in cloud environments that they don’t fully control, can’t always see, and that are constantly changing. Cloud-native assets—such as container workloads, autoscaling groups, and serverless functions—are highly dynamic, appearing, disappearing, and evolving in response to demand and functionality changes.

In this episode of the Security Matters podcast, host David Puner is joined by Lavi Lazarovitz, Vice President of Cyber Research at CyberArk Labs, to explore the transformative impact of AI agents on cybersecurity and automation. They discuss real-world scenarios where AI agents monitor security logs, flag anomalies, and automate responses, highlighting both the opportunities and risks associated with these advanced technologies.

Checkmarx is a popular SAST, DAST, and SCA provider that helps organizations detect and fix vulnerabilities and ensure application security. Its robust testing capabilities make it a go-to choice for many enterprises looking to integrate security into their DevSecOps pipeline. However, like all tools, Checkmarx has certain limitations. Some users find it expensive and complex to set up, while others report long scan times and occasional false positives, which slow down development workflows.

AI agents are revolutionizing how businesses operate by automating tasks, improving efficiency, and accelerating decision making. These intelligent systems leverage advancements in machine learning, natural language processing, and automation to handle complex workflows with minimal human intervention. Unlike traditional rule-based automation, AI agents can interpret unstructured data, follow contextual workflows, and integrate seamlessly with enterprise systems.

Snyk is exploring using the open-source Golang project Bento to read data from Kafka streams and materialize intelligence to various outputs. We are pleased to share that we are proactively helping secure the Bento project by contributing dependency fix updates.

2025 marks a pivotal moment. It’s the year AI agents transition from experimental technology to an essential business objective in enterprise operations that can enable growth and scale. These digital counterparts extend human intelligence, redefine workflows and create a new frontier in automation, cybersecurity and decision-making.

Artificial intelligence (AI) and generative AI (GenAI) are rapidly transforming the public sector, moving beyond theoretical possibilities to real-world applications. Proper data preparedness, stewardship, and governance will play critical roles in successful GenAI implementations. We recently hosted a webinar, Public sector data stewardship for the AI era, with industry experts Max Klaps, research director at IDC, and Dave Erickson, distinguished architect at Elastic.