Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Adding AI to your software development life cycle (SDLC) comes with great opportunities — and great dangers. Is the risk worth the reward? This was the topic of conversation when Sascha Wiswedel, Senior Solutions Engineer at Atlassian, and Simon Maple, Principal Developer Advocate at Snyk, teamed up to discuss security in the (AI-assisted) software development lifecycle.

A recent report from Mozilla found that the recent explosion of romantic AI chatbots is creating a whole new world of privacy concerns.

The UK's National Cyber Security Centre (NCSC), recently shared its findings on how AI might reshape the cyber landscape. In two separate posts, the NCSC is warning that the global ransomware threat is expected to rise with AI. It appears that while AI beckons with one hand, it wields a knife in the other. On one side, we have AI's potential to supercharge economic growth, scientific breakthroughs, and societal benefits. On the flip side lurks the specter of security risks posed by AI's misuse.

1. Personalization: AI algorithms can analyze large amounts of data scraped from social media, public databases, or leaked information to create highly personalized phishing emails. These emails may contain accurate details about the target’s interests, job positions, or recent activities, making them more convincing.

The AI revolution that kicked into overdrive last year has taken the world by storm, and as the development and productization of Large Language Models and other AI technologies continue – the revolution shows no signs of slowing down.

In a significant stride towards enhancing the capabilities of LangChain, the team introduces LangGraph, a module designed to facilitate the creation of cyclical graphs within the LangChain ecosystem. This novel addition, completely interoperable with LangChain, aims to empower developers in building agent runtimes with greater flexibility and efficiency.

So many logs. So little space. If you’re like most people running an Amazon Web Services (AWS) environment, then you probably have a vast collection of log files that include things like VPC flow logs and CloudWatch data. As if that’s not enough, you’re also collecting information about everything and everyone else connected to your cloud, like users, devices, network devices, applications, and APIs.

APIs are often adopted by businesses as a way to automate certain operational tasks. This not only helps to introduce efficiencies, it also reduces the chance of human error in repetitive, manual actions. But the relationship between APIs and automation doesn’t end there. To streamline the API management process, developers have started automating a variety of tasks in the API lifecycle, from development to production.