Mitigating Generative AI Security Risks: Strategies for Organizational Resilience

There is no denying that organizations are harnessing the potential of Generative AI at scale. Its capability to generate new, original content, whether it is text, image, audio, or video, is instrumental in its widespread use across diverse industry sectors. 

Did you know?

Salesforce’s latest State of IT report found that 67% of IT leaders surveyed said they have prioritized the use of Generative AI in their organizations. 

Generative AI is a double-edged sword. On one hand, it offers transformative benefits to organizations, which include personalization, realistic simulations, improved customer experiences, and time and cost savings. 

On the other hand, Generative AI poses significant security risks with its ability to create manipulative content. It can generate deep fakes and synthetic identities, leading to Impersonation, privacy threats, misinformation, etc. 

In the same report mentioned earlier, 

Every 2 in 3 respondents (65%) said that they can’t justify implementing Generative AI due to several barriers. One of the foremost reasons was the high chances (approx 71%) of security risks.

The security risks of Generative AI can serve as a bane for organizations. As a result, it becomes essential for them to take preventive measures. In this blog, we shall walk you through some major security risks Generative AI poses and effective ways to mitigate them. 

Major Security Risks in Generative AI

The following are some major risks associated with Generative AI that significantly affect personal, corporate, and national security:  

1. AI-Generated Deep Fakes

The foremost security risk of Generative AI is its ability to create hyper-realistic, convincing fake images, videos, audio, etc. This fake content serves as a critical source for creating misleading information, false narratives, and impersonation, leading to a damaged reputation and trust. 

To tackle the risks associated with deep fakes and fabricated content, organizations require a multi-faceted approach, including public awareness, authentication measures, and detection tools to pinpoint inconsistencies. 

Example: 

Let us understand the threat of AI-generated deep fakes with a real-world example. 

In March 2019, the CEO of a UK-based energy firm received a call impersonating his boss, the leader of the firm’s German parent company. The person on the call ordered the transfer of  €220,000 to a supplier in Hungary. Since the CEO recognized the slight German accent and the melody of his chief’s voice, he initiated the transfer of the stated amount. 

After the first successful attempt, the caller (threat imposter) tried multiple times for the second round of money. However, it failed as the CEO had grown suspicious and did not make any transfers.  

This event resulted in financial loss for the energy firm. 

To combat this type of risk, it is recommended to use a semantic password for conversations or start a call with a secret question. For a voice authentication service or biometric security features, ask the providers to keep the tools up-to-date. More importantly, organizations must educate their employees about deep fake attacks. 

2. AI Model Poisoning/Adversarial Attacks

AI model poisoning or adversarial attacks take place when threat imposters compromise training data fed to an AI model by inserting malicious content. They manipulate training data with misleading information and trick the AI model into generating harmful outputs, leading to incorrect decisions for businesses. 

The impact of adversarial attacks is adverse on applications like financial decision-making, health diagnostics, facial recognition for security purposes, recommender systems, and autonomous vehicle systems. Any errors or unexpected behavior of these applications can lead to severe consequences. 

3. Model Theft  

Model theft is an attempt carried out by attackers to steal proprietary AI models. When attackers gain access to the AI model, it is possible for them to deactivate data privacy measures and exploit the weaknesses. Moreover, they can utilize the proprietary model for unethical purposes, resulting in an organization’s reputational damage. 

Example:

One common scenario of model theft is an attacker exploiting a vulnerability in a company’s infrastructure to gain unauthorized access to proprietary AI models. Once they gain access, attackers can manipulate the model, use it to launch a completely different but similar kind of model, or extract sensitive information, leading to financial and reputational harm. 

To prevent this kind of theft, organizations should implement strong role-based access controls (RBACs) and robust authentication mechanisms to limit access to proprietary AI models and training data. 

4. Phishing Attacks 

We are already aware of GenAI’s misuse for creating deep fakes and fabricated content. Attackers are significantly leveraging it to generate emails and messages that are challenging to distinguish from legitimate ones. This poses an increased risk of email phishing and spoofing, clone phishing, social engineering attacks, etc.  

5. Training Data Leakage

Training data leakage refers to a situation wherein confidential or sensitive data, such as personal information or intellectual property, from training data, is unintentionally generated as the AI model’s output. This may happen due to several reasons as follows: 

• When the AI model memorizes specific inputs rather than the generalized ones. 
• Biases and identifiable information are present in the training data. 
• Lack of diversity in the training dataset. 

As data leakage exposes sensitive information, it results in compromised data privacy and unintended disclosures. 

To avoid this concern, it becomes essential for organizations to implement regularization techniques, remove confidential information from the training data, and ensure the diversity of the training dataset.  

6. Data Privacy Concerns

Data privacy concerns imply to both organizations and individuals. GenAI models require huge volumes of data for training, which may also include organizational proprietary data. If a GenAI model is not controlled, secured, or developed responsibly, there are high chances of the leakage and exploitation of organizational data. 

When it comes to individuals, they sometimes provide their personal or sensitive information as inputs to the GenAI model. This is true in terms of customer service chatbots and personalized content recommendations. Since GenAI models learn from user inputs, there is a heightened risk of user data exposure. 

Mitigating Generative AI Security Risks: 4 Effective Strategies 

Let us now take a look at some effective strategies to prevent or address the above security risks in Generative AI.

1. AI Governance Frameworks 

An AI governance framework refers to a set of policies, standards, regulations, and best practices used to facilitate the responsible and ethical development of AI systems. It ensures that organizations develop AI systems in accordance with legal standards, security implications, and regulatory complaints. 

A robust AI governance framework consists of several components, as follows: 

• Establish ethical guidelines
• Ensure data security and privacy
• Implement accountability mechanisms 
• Regulatory compliance 
• Proactive monitoring and assessment 

2. Data Anonymization and Encryption 

Data anonymization is a technique for preserving private and confidential data by removing or encrypting personally identifiable information from the training datasets. Data encryption, on the other hand, refers to transforming data into ciphertext (unreadable text) that can decoded only using unique decryption keys. Both these techniques safeguard private/sensitive data from getting breached or exposed. 

3. Invest in Cybersecurity Tools

Cybersecurity tools come in handy to mitigate the security risks posed by Generative AI. These tools can easily pinpoint anomalies, unexpected events, or any malicious activities in AI systems. Consequently, organizations can take measures accordingly to prevent both data and financial losses. 

Further, cybersecurity tools are often equipped with advanced encryption techniques, safeguarding training data from breaches. They also incorporate access control mechanisms and authentication, limiting access to privacy-sensitive information. Many of them integrate with vulnerability scanning and patch management systems to identify and address security vulnerabilities and weaknesses. 

In a nutshell, cybersecurity tools serve as a strong protection layer for Generative AI systems. 

4. GenAI Security Risks Awareness Training 

Awareness training must be on top of every organization’s security risk-mitigating strategies. This ensures the preservation of organizational proprietary data and individual data privacy. 

Effective training covers the implications of GenAI model biases, ethical considerations around AI-generated content, ways to identify security threats, and best practices to develop and deploy GenAI models responsibly. It is instrumental in preventing data breaches and misuse of GenAI tools. 

Conclusion

With multiple revolutionary benefits, Generative AI significantly poses security risks that can lead to intellectual property theft, reputational damage, data breaches, and financial losses. However, proactive strategies like establishing AI governance frameworks, data anonymization & encryption, utilizing cybersecurity tools, and employee awareness training can assist organizations in mitigating the above Generative AI security risks.

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We take pride in developing AI systems responsibly utilizing our governance framework, allowing you to harness the power of GenAI with peace of mind. Our team assists organizations in determining their AI readiness, helping them adopt AI technologies without any hassle. In addition, we boast GenAI products intended to solve your organization’s knowledge management challenges.