AI Security Risks: Top AI Security Concerns and How to Mitigate Them

AI enables organizations around the globe to work more efficiently, make better decisions, reduce errors, and improve their bottom line. But AI can be a double-edged sword when it comes to cybersecurity. Along with these AI benefits comes increased risk. Cyber attackers exploit AI to enact more complex attacks and do greater damage. Simply incorporating AI into your organization’s workflow increases your attack surface and enhances your security risk.

To protect your organization and use AI responsibly, it’s imperative to have measures in place to limit your AI security risks. Read on to learn all about AI and security, potential vulnerabilities, and how to protect your business.

• What Is AI and How Does It Work?
• The Intersection of AI and Security
• 4 Major AI Security Risks
• How to Mitigate AI Security Risks
• How Gigamon Helps Mitigate AI Security Risks
• Conclusion

Key Takeaways

• AI uses algorithms to simulate human intelligence and complete tasks
• While AI can enhance cybersecurity measures, it can also help cyber attackers carry out more advanced attacks
• Common AI security risks can result in biased decision-making, exposure of PII, unlawful access, and advanced phishing schemes
• Network visibility from Gigamon is key to protecting your organization from AI-powered cyberattacks

What Is AI and How Does It Work?

Artificial intelligence (AI) is a technology that uses algorithms to simulate human intelligence and complete tasks. Depending on the type of AI, this technology may be capable of behaviors previously only believed to be human, like reasoning, problem-solving, and understanding natural human language.

While AI may appear to have human intelligence, it’s powered by algorithms that ingest huge amounts of data, look for patterns, and make decisions based on those patterns. Depending on the type of AI, its capabilities may range from following rule-based tasks to complex reasoning and decision-making.

Types of AI include:

• Machine learning: AI powered by machine learning is capable of learning from past experiences and using that information to improve its performance.
• Generative AI: Generative AI is capable of creating new content based on data. It uses datasets to create images, text, music, and more.
• Large Language Models (LLMs): This type of generative AI is capable of both understanding natural human language and generating text that mimics natural language.

The Intersection of AI and Security

When it comes to AI and security, AI can be used as both a tool to defend against cyber attacks and a vector with which to enhance attacks. In terms of defense capabilities, AI can be used to analyze network information and identify potential threats. It can also help look for areas of vulnerability or examine past cybersecurity events and use the information to predict future ones.

On the other hand, AI increases the attack surface. It can be used to create more sophisticated attacks, generate new versions of malware, and quickly identify vulnerabilities for cyber attackers to exploit.

4 Major AI Security Risks

This new technology leaves organizations vulnerable to a number of unique AI security risks. Read on to learn the most common vulnerabilities that you need to be aware of.

1.   Adversarial Attacks

Adversarial attacks involve feeding AI data with slight modifications that are nearly impossible to detect. They can result in flawed outputs and decision-making. For instance, an attacker may be able to slightly manipulate an image to deceive AI image recognition or gain unlawful access to cybersecurity tools.

2.   Generative AI Misuse

The increasingly complex capabilities of generative AI pose a significant security risk. Cyber attackers can use generative AI to create extremely convincing deepfakes, nearly undetectable phishing emails, or even create more advanced versions of malware code. Because generative AI operates off a learning model, its capabilities in doing so improve every day, increasing the number of generative AI security risks and their efficacy.

3.   Data Poisoning and Model Manipulation

These malicious behaviors use AI to create problems in an organization’s data and models. Data poisoning involves training the AI model on false or misleading information in order to corrupt its learning process. It can lead to poor performance, lower accuracy, predictive errors, or biased outcomes. Model manipulations, on the other hand, involve changing the AI model itself to worsen performance or introduce vulnerabilities that can later be exploited. 

4.   Model Inversion and Privacy Leaks

In this security risk of artificial intelligence, attackers use model outputs to reverse-engineer and even reconstruct training data. In doing so, attackers can expose sensitive data like personally identifying information or data protected by compliance regulations. It may also uncover proprietary information used during training.

How to Mitigate AI Security Risks

AI can be an extremely beneficial tool when used correctly, but it exposes organizations to increasingly advanced attacks. Use these best practices to mitigate security risks and protect your organization from artificial intelligence-driven security incidents.

1.   Establish AI Governance and Risk Management Frameworks

To use AI responsibly, it’s important to create policies that dictate its management and use. These include defining how and when AI will be used and who will oversee it, as well as addressing compliance requirements and developing security protocols.

A strong risk management framework begins with assessing all possible risks and vulnerabilities presented by AI. It should also involve cross-functional input from teams, including IT, legal, and compliance. Once a framework is in place, it should be continuously monitored for its efficacy and modified accordingly. Just as AI is ever-changing, so are the risks associated with it. Review and update your framework regularly to ensure it remains effective against evolving threats.

2.   Monitor AI Traffic and Behavior

The increased attack surface associated with AI makes cyberattacks increasingly likely. Network traffic must be monitored 24/7 to prevent a covert attacker from slipping through the cracks.

For this, leverage the network visibility of Gigamon, which works around the clock to detect anomalies in your network. The Gigamon Deep Observability Pipeline provides visibility into all network traffic at once to uncover hidden threats and strengthen your security posture. This tool can minimize your cybersecurity blind spots and detect unauthorized data movement or model behavior, trademark signs of artificial intelligence-driven security incidents.

3.   Secure Data Pipeline and Training Inputs

The AI training process presents a unique AI security concern because of its potential for exploitation. It’s important to put robust security measures in place throughout your data pipeline and training inputs, like data validating, encryption, and secure storage. Limiting access is vital, too, through rule-based access control and a Zero Trust Architecture.

To ensure your security measures are doing their job, conduct regular audits of training datasets. Check the quality of the training data, look for potential bias, and verify compliance.

4.   Educate and Train Employees on AI Security Risks

Your AI security strategy is only as strong as the people who use it. It’s imperative to thoroughly train employees on the security risks of artificial intelligence and the measures you have in place to protect against them. Focus on AI security concerns surrounding the misuse of generative AI and advanced phishing tactics, the most common AI security risks the average employee is likely to come into contact with.

Because AI considerably expands your attack surface, it’s important to restrict employee access to vetted AI tools only. The use of potentially exploitable AI tools on company devices leaves your organization exposed and unprotected.

How Gigamon Helps Mitigate AI Security Risks

The Gigamon Deep Observability Pipeline enhances network security monitoring by providing deep visibility into network activity to identify AI security concerns before they can cause damage. It offers comprehensive observability into East-West, North-South, and container traffic across hybrid cloud and on-premises environments.

Gigamon works behind the scenes to decrypt encrypted traffic, detect anomalous AI behavior, and enforce security policies in real time. Plus, it seamlessly integrates with your existing AI/ML security solution for greater threat detection and faster response.

Conclusion

In the world of cybersecurity, AI presents a double-edged sword. While organizations leverage AI capabilities to develop more advanced security measures, attackers use it to create more advanced attacks. To avoid this cat-and-mouse game, it’s imperative to know the common risks associated with AI and have measures in place to mitigate incidents.

Rather than leaving your organization vulnerable to security threats, stop artificial intelligence-driven security risks with enhanced visibility. The Gigamon Deep Observability Pipeline allows you to gain deep insight into your network traffic at all times and stop cyberattacks in their tracks. Learn how Gigamon can help your organization stay secure in the age of AI.

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