What is Cortex Cloud AI Security? - A basic overview of the Cortex Cloud AI Security overview page, assets inventory, risks, and benefits. - Administrator Guide - Cortex CLOUD

Comprehensive Visibility: Obtains a full picture of AI components, including models, agents, data flows, and infrastructure across all cloud environments. This broad visibility ensures that every AI asset is accounted for and continuously monitored, reducing blind spots in the AI ecosystem.

Full supply chain protection: Maps the dependencies between data, models, and cloud resources to remediate risks such as poisoned datasets or unsanctioned models. Maintains the integrity of your AI bill of materials (AI-BOM).

Detailed asset inventory: Access an in-depth inventory of all AI assets, enriched with contextual details. This deep insight into each asset’s specifics and functionalities facilitates a better understanding and more effective management of these resources.

Advanced risk assessment: Proactively identifies and issues alerts on misconfigurations and security flaws in AI assets. Cortex Cloud AI Security employs sophisticated detection mechanisms to tackle risks associated specifically with AI, managing permissions, and ensures robust security practices are upheld throughout the AI supply chain.

Dynamic risk prioritization: Utilizes insights into data sensitivity and the broader security context to effectively understand and prioritize risks. This strategic approach enables organizations to target and mitigate the most critical threats swiftly, thereby enhancing the overall security landscape.

Governance and control: Implements comprehensive guardrails and controls for AI models both during development and in production. Ensures that AI assets operate within defined security parameters, reducing the likelihood of security breaches and data leaks.

Compliance assurance: Regularly tests AI systems against emerging AI regulations and industry standards, such as the OWASP Top 10 for Large Language Models (LLMs). Gets clear guidelines on corrective actions needed to achieve full compliance and ensures that AI assets align with both current and future regulations.

Data Poisoning Attacks: In "Training Data Poisoning", malicious actors manipulate the training data to introduce biases or vulnerabilities into the model, causing it to make incorrect or harmful predictions.

Model Inversion Attacks: Attackers can infer sensitive information about the training data by querying the model, potentially leading to data breaches and loss of intellectual property.

Adversarial Attacks: Crafted inputs can deceive the model into making incorrect predictions, which is particularly dangerous in critical applications like autonomous driving or medical diagnosis.

Evasion Attacks: Evasion attacks are a prevalent threat to machine learning models during inference. This type of attack involves crafting inputs that appear normal to humans but are misclassified by machine learning systems. For instance, an adversary might alter a few pixels in an image prior to submission, causing an image recognition system to misidentify it.

Model Extraction Attacks: Attackers can approximate a model's functionality by repeatedly prompting it, effectively stealing the intellectual property and potentially using it for malicious purposes.

Data Leakage: If a model unintentionally reveals sensitive information it was trained on or data that is used in inference, it can lead to breaches of confidential or personal data.

Model Manipulation: Unauthorized access to the model can allow attackers to alter its parameters or behavior, leading to compromised functionality and trustworthiness.

Inference Attacks: Attackers exploit the model to deduce whether specific data was part of the training set, potentially exposing sensitive information.