Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

From the moment ChatGPT was released to the public, offensive actors started looking to use this new wealth of knowledge to further nefarious activities. Many of the controls we have become familiar with didn’t exist in its early stages. The ability to request malicious code or the process to execute an advanced attack was there for the asking from an open prompt. This proved that the models could provide adversarial recommendations and new attacks never before seen.

There is a pressing need to protect an organisation’s digital assets against cyber attacks and it has never been more critical. The increasing complexity and dynamic nature of IT environments mean that traditional security measures often fall short. This has led to the emergence of new defensive approaches, such as attack surface management (ASM) that proactively safeguard against cyber threats.

You trust us with your most important workflows, and we take that trust seriously. In developing AI in Tines, we’ve been laser-focused on helping users leverage AI without exposing their organizations to security and privacy risks. But we also spoke with so many teams struggling to fully realize AI's potential impact. They wanted AI to do more, while still preserving those all-important security and privacy guardrails.

This is the second in a series of articles about cloud-based attack vectors. Check out our last article about admin takeovers! Inside the Cloud: Attacks & Prevention – Administrative Account Compromise Ransomware has long been associated with takeovers of endpoints. However, attackers are evolving to target cloud environments – and the effects can be devastating.

Consider the case of a malicious actor attempting to inject, scrape, harvest, or exfiltrate data via an API. Such malicious activities are often characterized by the particular order in which the actor initiates requests to API endpoints. Moreover, the malicious activity is often not readily detectable using volumetric techniques alone, because the actor may intentionally execute API requests slowly, in an attempt to thwart volumetric abuse protection.

AI guardrails are vital for ensuring the safe and responsible use of AI/large language models (LLMs). However, focusing solely on single prompt-level checks can leave organizations vulnerable to sophisticated threats. Many company policy violations and security risks can be cleverly split across multiple, seemingly innocent queries. To effectively protect against these threats, a more comprehensive approach is needed — session-level monitoring.

As businesses increasingly adopt Generative AI (Gen AI) to enhance operations, customer engagement, and innovation, the need for robust AI guardrails has never been more critical. While Gen AI offers transformative potential, it also introduces significant risks that can jeopardize your business if not properly managed. Below, we explore five critical risks associated with Gen AI and provide strategies to avoid them.