Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Revolutionizing Security: AI at the Heart of Modern Protection

Dive into the future of security with us at Brivo as we explore how AI-Centric Security is transforming the way we protect spaces in real-time. Join Neerja Bajaj in uncovering the power of artificial intelligence in analyzing security data, identifying threats, and responding with unmatched efficiency. From commercial real estate to multifamily residential areas, discover how Brivo leverages cutting-edge AI to ensure your safety and peace of mind.

How Brokers Harness Artificial Intelligence for Market Analysis

The integration of artificial intelligence (AI) in the finance sector has seen a dramatic surge over the past decade. Key technological advancements like increased computing power, improved algorithms, and the availability of big data have paved the way for AI to transform brokerage operations.

"AI is only useful when it solves real customer problems": Tines on Risky Biz

We’re all huge fans of the Risky Biz podcast here at Tines, so we were thrilled to be invited to appear on the show recently to talk about AI’s role in security automation. I had a great conversation with host Patrick Gray about the security and privacy challenges that go along with deploying an LLM in your environment, and how our approach to AI in Tines is fundamentally different. I loved every minute of this chat, and I hope you’ll find it interesting, too.

4 AI coding risks and how to address them

96% of developers use AI coding tools to generate code, detect bugs, and offer documentation or coding suggestions. Developers rely on tools like ChatGPT and GitHub Copilot so much that roughly 80% of them bypass security protocols to use them. That means that whether you discourage AI-generated code in your organization or not, developers will probably use it. And it comes with its fair share of risks. On one hand, AI-generated code helps developers save time.

Transparency and Ethics in AI: Ensuring Safety and Regulation

In this video, Erin Mann delves into the critical importance of transparency and ethics in the use of artificial intelligence (AI). As AI continues to evolve and integrate into various aspects of our lives, ensuring its ethical use and safety becomes paramount. Erin discusses how transparency in AI operations can drive the necessary conversations around regulation and efficient implementation. By understanding the ethical implications and advocating for clear guidelines, we can harness the power of AI responsibly and effectively.

Is over-focusing on privacy hampering the push to take full advantage of AI?

Customer data needs to be firewalled and if protected properly can still be used for valuable analytics In 2006, British mathematician Clive Humby declared that data is the new oil-and so could be the fuel source for a new, data-driven Industrial Revolution.

AI quality: Garbage in, garbage out

If you use expired, moldy ingredients for your dessert, you may get something that looks good but tastes awful. And you definitely wouldn’t want to serve it to guests. Garbage in, garbage out (GIGO) applies to more than just technology and AI. Inputting bad ingredients into a recipe will lead to a potentially poisonous output. Of course, if it looks a little suspicious, you can cover it in frosting, and no one will know. This is the danger we are seeing now.

How AI adoption throughout the SDLC affects software testing

With AI finding adoption throughout all stages of the development process, the SDLC as we know it is becoming a thing of the past. Naturally, this has many implications for the field of software testing. This article will discuss how the SDLC has evolved over time, going into detail on the impact that AI adoption is having on both software development and software testing.

Protecto Announces Data Security and Safety Guardrails for Gen AI Apps in Databricks

Protecto, a leader in data security and privacy solutions, is excited to announce its latest capabilities designed to protect sensitive enterprise data, such as PII and PHI, and block toxic content, such as insults and threats within Databricks environments. This enhancement is pivotal for organizations relying on Databricks to develop the next generation of Generative AI (Gen AI)applications.

Enhancing Language Models: An Introduction to Retrieval-Augmented Generation

Over the past few years, significant progress has been observed in the area of NLP, largely due to the availability and excellence of advanced language models, including OpenAI's GPT series. These models, which are useful for generating human-like text which is contextually appropriate, have transformed several interfaces from conversational agents to creative writing. However, as popular and effective as they may seem, the traditional language models have their own drawbacks and specifically, the restriction in accessing additional up-dated data and incorporating them.