Resources

Resources by AIShield

Here you can find Whitepapers, Case Studies, and Talks on AI security topic

AIShield Overview

Overview on AI Security and AIShield's offerings

Media

Solution Case Studies

Read more to understand how AIShield has helped in various domains

Media

Digital Brochure

RSAC 365

Fighting New Era Bandits Who Are Plundering Your AI/ML Application

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Frequently Asked Questions

Are there any upcoming regulations and standards regarding AI Security?

Why do organizations need to secure AI assets?

With the advancement in technology, there have been increasing AI algorithm failures. The complexity and number of such losses have been growing every day. Looking at some of the facts and current industry scenario indicates the need for AI Security, and correct measures must be in place before a significant model failure.

According to Microsoft, 89% of organizations have the right tools to secure their AI.
Gartner found that 60% of AI providers will include a means to mitigate possible harm to their AI assets by 2024
IBM quotes that 80% cost difference between cyberattack scenarios, where secure AI was developed vs. not deployed.

What is AI Security?

AI security refers to the domain of securing AI systems. One can also refer to cybersecurity for AI applications & models/algorithms. AI Security focuses on identifying, detecting, preventing, remediation, information, investigating and responding to malicious attacks on AI Systems. The field is concerned with AI model vulnerabilities, studying attacker capabilities, consequences of attacks, and building algorithms that can resist security challenges.

How is AIShield different from my existing cybersecurity measures?

AIShield is the last layer of defense in an existing device, network & application security. It protects AI models, which are highly invested and most valuable assets of an AI system. AI model and system security is not covered under existing Cybersecurity measures since AI security attacks can happen as part of an AI application’s input, output, and data payload where the hacker poses as a legitimate user.

What are various AI/ML adversarial attacks?

AIShield protects AI systems against four types of adversarial attacks.

Model Extraction – The act of stealing and extracting a proprietary model

Model Evasion – The act of making a model do the wrong thing making it intentionally biased

Data Poisoning – The act of making a model learn wrong things by injecting malicious samples

Model Inference – The act of making the model reveal its logic and data