Generative AI Security Risks, Challenges, and Best Practices

Generative AI has transformed how businesses think over the past year, including their security posture. But while much of the discussion focuses on AI’s ability to facilitate cyberattacks, there’s also tremendous opportunity in combining generative AI and cybersecurity best practices to protect businesses.

For example, attackers are leveraging generative AI to craft more convincing phishing emails than ever, taking advantage of the ability to generate realistic content, mimic trusted sources and personalize messages to deceive unsuspecting recipients. Meanwhile, savvy businesses can also use innovative AI applications to evolve their security defenses and stay ahead of threats.

While most security professionals say generative AI is fueling an increase in attacks, that’s not the whole picture. Generative AI is also a “force multiplier” for security, says IBM, with 84% of executives prioritizing cybersecurity solutions based on AI technologies.

“Companies that have built broad capabilities in both risk and resilience will be able to go farther faster with this new technology — and be better positioned to defend future growth,” the IBM report says.

We believe generative AI is a powerful ally for improving cybersecurity, especially when you have a trusted partner to help you implement the technology. Read on to learn how you can get started today.

Why Generative AI Forces Businesses to Revisit Security Strategies

Generative AI should have every business revisiting their security posture and policies, both to evaluate external threats and to take advantage of new internal capabilities.

For example, large language models (LLMs) help attackers craft seemingly authentic emails that can trick your employees into granting unauthorized access or revealing sensitive information. Traditional security measures, such as email filters, may struggle to detect AI-enhanced threats. At the same time, AI-powered tools could improve your ability to detect and prevent these threats — and do so quickly and automatically.

The vast number of available AI models is already challenging for businesses to assess, with more models emerging every week. Your security teams need a plan for understanding which models can help secure the business, which don’t make sense for your needs — and which are potential threats.

The human element remains a critical vulnerability. Despite extensive training, employees can still fall victim to sophisticated social engineering tactics and phishing attacks. AI-powered attacks can worsen this problem, but the technology can also bolster your security efforts and provide an extra layer of training for employees.

Finally, no matter how your business is implementing AI, you need strong measures to protect privacy, safeguard data and control access. Strong security measures, including encryption and access controls, can mitigate the risks associated with AI-generated attacks while allowing for safe use of generative AI within your organization.

8 Ways to Use Generative AI for Cybersecurity

Remember that generative AI isn’t just a threat. Look for the opportunities to positively introduce this technology into your organization’s processes, mindset and security posture. Here are eight actions you can take when using generative AI in cybersecurity.

1. Understand and Evaluate Your Risks

Like anything related to security, you should start by understanding and evaluating your risk landscape. Awareness is a good start, but ultimately, you want to foster a security culture within the organization. This type of culture grounds all practices in a security mindset — every product and service considers security, and every employee understands how to uphold security. Moreover, security isn’t just about one layer but spans the entire tech stack.

Start by familiarizing your team with common AI cybersecurity resources, such as the Open Worldwide Application Security Project’s top 10 vulnerabilities in LLMs. This free resource can help your team understand the latest vulnerabilities and how to mitigate them while promoting a strong LLM security posture.

2. Develop an LLM Policy

Because generative AI is here to stay, businesses need to decide how they’ll assess and implement LLMs, as well as what guardrails are appropriate. Don’t wait; be proactive about LLM policies, including designating any systems that are off-limits.

Taking a holistic view is crucial. Evaluate which services are secure and can be run within their own environment and ecosystem. For example, businesses operating within the AWS ecosystem may require the use of Amazon CodeWhisperer, which can scan your code and identify vulnerabilities you might otherwise miss.

3. Use the Right Tools

There are countless AI tools on the market, but only some of them make sense for your business. Turn to existing security tools for your AWS environment whenever possible to protect your account and your applications. In fact, Amazon has created a thorough security scoping matrix for determining which security disciplines apply to your generative AI solutions.

4. Dig Deeper Into the Data

With an abundance of intelligence, logs, and audit trails available, organizations can use LLMs to delve deeper into this data and extract valuable insights. By feeding this data into generative AI models, defenders can quickly identify attack patterns and assess the level of risk associated with each attack.

Generative AI models can quickly spot attack patterns and help you respond immediately, rather than only after an incident. This empowers security operations center analysts to focus on the most important risks rather than being overwhelmed by the sheer volume of alerts.

5. Embrace Predictive Analytics

Threat activity often follows repetitive patterns or well-known execution steps. By continuously analyzing vast amounts of data, generative AI algorithms can identify patterns and anomalies that indicate potential cyberthreats. These algorithms can learn from historical data and detect subtle changes in network behavior, user activity or system configurations that may signify an impending attack.

With this predictive ability, businesses can implement preventive measures to mitigate risks and protect their systems and data. For example, if AI-powered analytics detect a pattern of unauthorized access attempts from a specific IP address, the organization can immediately block that IP address and strengthen authentication protocols to prevent further unauthorized access.

6. Focus on Data Authenticity and Integrity

Generative AI can help businesses quickly and accurately verify the legitimacy of incoming data. Use algorithms to authenticate data you’re receiving, sanitize that data and validate it for your business needs. You want to ensure you’re working with legitimate and reliable information so there’s minimal risk to employees, customers and the business itself.

7. Automate Security Alerts, Responses and Reporting

AI-powered automation systems can perform level one triage, automatically filtering out those alerts. This relieves the burden on your security analysts, who can focus on level two alerts. Your team improves productivity and spends their time on work that truly requires their expertise and intervention.

Another use case for automation is report writing, which can take significant time for SOC analysts when documenting incidents, conducting investigations and performing other security-related activities. Automation can take on much of this report generation, with analysts reviewing the AI-generated material and filling in the gaps rather than doing everything from scratch. As a bonus, your team gets more time for critical tasks such as threat hunting and incident response.

8. Foster a Culture of Continuous Learning

Generative AI is a rapidly evolving field, and just as your cybersecurity experts are forever learning and adapting, so must your larger workforce. In particular, employees must understand your company’s policies regarding the information they provide to public AI tools, such as ChatGPT. Employees should be educated on the potential risks associated with sharing sensitive information with AI models, especially those that aren’t controlled by the organization.

Emphasize the need for caution and continuous vigilance. Just because someone spotted phishing attempts in the past doesn't guarantee they’ll recognize the next one. Cyberthreats are constantly evolving, and attackers are becoming ever-more sophisticated.

Embrace Generative AI’s Potential for Cybersecurity

It’s easy to focus on the security downsides of generative AI. But remember that companies can also use the technology for good. Look at how generative AI tools can bolster your security posture, add new capabilities, improve efficiencies, reduce human error and more.

As you explore generative AI’s possibilities, especially within your AWS environment, Mission Cloud’s in-depth expertise and unparalleled support can help. Get in touch with our cloud advisors to learn how to unlock the future of the cloud with AWS.

Want to see Mission Cloud in action? Check out how we helped EV Connect prepare for strict federal compliance requirements for electric vehicle charging stations so the company could grow its customer base and expand into new markets.