Welcome to CloudSec Academy, your guide to navigating the alphabet soup of cloud security acronyms and industry jargon. Cut through the noise with clear, concise, and expertly crafted content covering fundamentals to best practices.
In this guide, we’ll break down why AI governance has become so crucial for organizations, highlight the key principles and regulations shaping this space, and provide actionable steps for building your own governance framework.
The short answer is no, AI is not expected to replace cybersecurity or take cybersecurity jobs. It will, however, augment cybersecurity with new tools, methods, and frameworks.
AI data security is a specialized practice at the intersection of data protection and AI security that’s aimed at safeguarding data used in AI and machine learning (ML) systems.
LLM models, like GPT and other foundation models, come with significant risks if not properly secured. From prompt injection attacks to training data poisoning, the potential vulnerabilities are manifold and far-reaching.
Data leakage is the unchecked exfiltration of organizational data to a third party. It occurs through various means such as misconfigured databases, poorly protected network servers, phishing attacks, or even careless data handling.
ChatGPT security is the process of protecting an organization from the compliance, brand image, customer experience, and general safety risks that ChatGPT introduces into applications.
AI risk management is a set of tools and practices for assessing and securing artificial intelligence environments. Because of the non-deterministic, fast-evolving, and deep-tech nature of AI, effective AI risk management and SecOps requires more than just reactive measures.
Adversarial artificial intelligence (AI), or adversarial machine learning (ML), is a type of cyberattack where threat actors corrupt AI systems to manipulate their outputs and functionality.
LLM jacking is an attack technique that cybercriminals use to manipulate and exploit an enterprise’s cloud-based LLMs (large language models).
Prompt injection attacks are an AI security threat where an attacker manipulates the input prompt in natural language processing (NLP) systems to influence the system’s output.
Data poisoning is a kind of cyberattack that targets the training data used to build artificial intelligence (AI) and machine learning (ML) models.
AI-SPM (AI security posture management) is a new and critical component of enterprise cybersecurity that secures AI models, pipelines, data, and services.
Dark AI involves the malicious use of artificial intelligence (AI) technologies to facilitate cyberattacks and data breaches. Dark AI includes both accidental and strategic weaponization of AI tools.
Learn about the most pressing security risks shared by all AI applications and how to mitigate them.
We’ll take a deep dive into the MLSecOps tools landscape by reviewing the five foundational areas of MLSecOps, exploring the growing importance of MLSecOps for organizations, and introducing six interesting open-source tools to check out
To manage risks associated with AI, organizations need a strategic and well-coordinated security approach that extends traditional cybersecurity measures to the unique needs of AI.
Shadow AI is the unauthorized use or implementation of AI that is not controlled by, or visible to, an organization’s IT department.
AI is the engine behind modern development processes, workload automation, and big data analytics. AI security is a key component of enterprise cybersecurity that focuses on defending AI infrastructure from cyberattacks.
