Published on November 27, 2023, 12:42 pm

Research indicates that generative AI is set to play a significant role in the business landscape. By 2026, it is projected that more than 80% of enterprises will be utilizing generative AI models, APIs, or applications. This represents a remarkable increase from the current adoption rate of less than 5%.

However, this rapid implementation of generative AI technologies raises important considerations in various areas including cybersecurity, ethics, privacy, and risk management. Despite the potential benefits, there are still challenges that need to be addressed.

One particular concern is the mitigation of cybersecurity risks. Surprisingly, only 38% of companies currently using generative AI take adequate measures to protect against these risks. As cyber threats become increasingly sophisticated, it is crucial for organizations to prioritize cybersecurity measures when utilizing generative AI technologies.

Another aspect that demands attention is model inaccuracy. Current statistics indicate that just 32% of businesses actively work towards addressing this issue. As generative AI becomes more pervasive within organizations, ensuring the accuracy and reliability of these models will be essential for maintaining efficient operations.

In my conversations with security practitioners and entrepreneurs, three key factors have emerged as essential considerations when implementing generative AI technologies:

1. Prompt injections: Businesses are recognizing the immense potential of customer-facing chatbots that are trained on industry-specific data. However, such chatbots are vulnerable to prompt injections – a form of injection attack where an attacker manipulates the model’s response or behavior. It is crucial for organizations to establish robust security measures to prevent such vulnerabilities.

2. Employee-led adoption: Chief information security officers (CISOs) and security leaders are increasingly under pressure to adopt generative AI applications within their organizations due to employee demand. This signifies a significant shift in the workplace dynamics where employees are actively driving technology adoption.

3. Data security tooling: The widespread use of genAI chatbots necessitates reliable methods for validating inputs and outputs without compromising the user experience. Current data security tools often rely on preset rules, leading to false positives. To overcome this, AI-based tools like Protect AI’s Rebuff and Harmonic Security leverage dynamic AI models to determine whether data passing through a genAI application contains sensitive information.

As generative AI continues to gain momentum in the business world, organizations must prioritize cybersecurity, address model inaccuracy, and consider other related factors. Embracing these considerations will ensure that businesses can leverage the full potential of generative AI while minimizing risks.

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