AI-driven technologies are reshaping the security landscape in businesses, posing new challenges for cybersecurity.
In a recent survey conducted by the Capgemini Research Institute, 1,000 organizations from 12 industries and 13 countries in Asia-Pacific, Europe, and North America, with an annual revenue of at least one billion USD, revealed their views on the role of Artificial Intelligence (AI) and Generative AI (Gen AI) in cybersecurity.
The survey results show that three out of five (61 percent) of the organizations consider AI indispensable for an effective response to threats. More than half of companies (52%) expect AI, particularly Gen AI, to help them detect threats faster and avoid mistakes.
However, the integration of Gen AI into cybersecurity brings both transformative potential and significant risks. Below is an overview of the major threats and key mitigation strategies organizations can adopt.
**Major Risks Associated with Generative AI in Cybersecurity**
1. **Increasingly Sophisticated and Scalable Attacks** - Self-Evolving Malware: Generative AI enables attackers to create malware capable of “self-evolving” in real time, making it more difficult for traditional security measures to detect and block new variants. - Scaling Attack Volume: AI allows cybercriminals to launch attacks at unprecedented scale, such as generating large volumes of convincing phishing emails or social engineering content in a fraction of the time previously required.
2. **Data Privacy and Leakage** - Unintentional Exposure: Employees may input sensitive or proprietary data into public generative AI platforms, leading to data leakage outside the organization’s control. - Model Training Risks: Data used to train or fine-tune generative models may inadvertently expose confidential information if not properly sanitized.
3. **Model Manipulation and Exploitation** - Prompt Injection: Attackers manipulate input prompts to bypass safety measures, extract sensitive data, or cause the AI to behave in unintended ways. - Model and Data Poisoning: Bad actors inject malicious data into training datasets to compromise model behavior.
4. **Malicious Code Generation** - Democratization of Hacking: Generative AI tools can produce malicious code, enabling individuals with minimal technical skills to launch sophisticated attacks. - Evasion of Security Controls: AI-generated malware and exploits can be tailored to evade detection by traditional security tools.
5. **Intellectual Property and Model Theft** - Model Theft: Proprietary AI models can be stolen via API exploitation or reverse engineering, leading to intellectual property loss and competitive disadvantage.
6. **Ethical, Legal, and Decision Integrity Risks** - Bias and Discrimination: AI models may reproduce or amplify biases present in their training data, raising ethical concerns. - Legal Liability: Use of copyrighted material for training or unclear accountability for harms caused by AI-generated content introduces legal risks. - Decision Integrity: Over-reliance on generative AI without human oversight can result in undetected errors or biases in automated decisions.
**Mitigation Strategies**
To mitigate these risks, organizations can implement a combination of technical controls, policy measures, and human oversight. Key strategies include:
1. **Implement Robust Data Governance** 2. **Strengthen Access Controls and Monitoring** 3. **Enhance Model Security** 4. **Foster Human Oversight and Collaboration** 5. **Address Legal and Ethical Concerns** 6. **Protect Intellectual Property**
By addressing these risks, organizations can better harness the benefits of generative AI in cybersecurity while minimizing its potential downsides. The survey also revealed that almost 6 in 10 companies believe it is necessary to increase their cybersecurity budget to strengthen their defenses, and two-thirds of organizations currently prioritize the use of AI in their cybersecurity operations.
The Capgemini Research Institute's study shows that new cybersecurity risks are emerging due to the widespread adoption of AI and Gen AI. Organizations must remain vigilant and proactive in managing these risks to ensure the secure and effective use of AI in their cybersecurity operations.
- The survey results indicate that a significant number of organizations (61%) view AI as crucial for effective threat response, with over half (52%) expecting Gen AI to speed up threat detection and minimize errors.
- Despite the transformative potential of Gen AI in cybersecurity, the integration of such technology poses major risks, including increasingly sophisticated and scalable attacks, data leaks, model manipulation, malicious code generation, intellectual property theft, and ethical, legal, and decision integrity concerns.
