How ChatGPT Reinvents Cybersecurity: Features & Prompt Examples

ChatGPT has gained a staggering 100 million monthly active users in just two months. And it’s hard to ignore AI’s potential impact on cybersecurity.

Cyber attacks have become more frequent, sophisticated, and damaging in recent years, but ChatGPT takes social engineering to a whole new level, making it much harder to keep systems secure. Since it’s also being used to automate various tasks ranging from marketing and accounting to web development, it rapidly replaces careers that previously required human intelligence. While this brings significant benefits, such as increased efficiency and cost savings, it also raises concerns about the potential impact of AI on employment, privacy, and security, including the use of AI in decision-making and the potential for bias and discrimination.

As the growth of AI’s potential to both enhance and threaten cybersecurity continues, it is vital for experts to prepare. At Onboardbase, we have always focused on making software security easier for modern engineering teams. AI presents numerous opportunities e are excited to address in future iterations of our product.

In this article, we glimpse how ChatGPT is reinventing cybersecurity and the potential implications of AI on the cybersecurity industry. Let’s look at 8 ChatGPT features that’ll change how we do cybersecurity.

8 AI Features For Cybersecurity

1. Threat Detection

Threat detection must handle ever-evolving cyber threats and vast amounts of data to identify security threats. While software already exists to cover some use cases like form spamming, ChatGPT can take things a step further by analyzing complex data relationships:

Behavioral analytics & anomaly detection - Large language models can analyze your app’s logs and detect potential threats by telling them what to look for―form spam, sensitive HTTP requests, etc.
Threat model classification - ChatGPT can be trained on official threat models to classify new threats in real-time, allowing them to detect problems before they occur.

2. Vulnerability Assessments

Vulnerability assessments involve analyzing large code repositories to identify potential attack vectors. ChatGPT can be great to help solve this because it can quickly analyze and understand the relationships between different parts of a system or application and can identify potential vulnerabilities or weaknesses that may not be immediately apparent to human analysts.

Scan codebase - ChatGPT can analyze your codebase using OpenAI embeddings to identify vulnerabilities based on best practices and previous vulnerabilities in similar codebases.
Tell ChatGPT how to assess priorities - Vulnerabilities can then be prioritized based on metrics like the potential impact on the system or the risk probability.

3. Incident Response

Incident response aims at minimizing the impact of security breaches: fixing a data breach in less than 200 days can save $1.12M on average, according to a study by IBM.

It is a challenging task because it requires quick decision-making based on incomplete and rapidly changing information. As we saw earlier, ChatGPT can identify active threats and passive vulnerabilities and assist cybersecurity experts in quickly containing breaches.

Automated updates & bug fixes - After uncovering vulnerabilities with ChatGPT, we can ask it to suggest fixes or package updates.
Alerting & flag management - We can also use ChatGPT to automate incident response processes in a data breach by isolating infected systems using flags and sending smart alerts to the relevant developers. ChatGPT assists with incident reporting and communication with all stakeholders, which is critical for coordinating an effective response across different teams while complying with legal requirements to inform customers.

4. Software Architecture

Secure software architecture requires thorough planning, design, and implementation to ensure your software is resilient against potential security threats.

Security best practice reviews - Language models can recommend secure coding practices like using parameterized database queries or enforcing input validation.
Security requirements - ChatGPT can write security requirements from a simple list of best practices in any format and scenario.
Secure code development - As proven by tools like Github Copilot, an advanced natural language model can generate and explain secure code based on pre-defined security requirements, including functional and unit tests.

5. DevSecOps

DevOps pipelines have considerably sped up development workflows over the last years but also created new attack vectors as well: DevSecOps is the practice of integrating security best practices into DevOps processes―testing, secure code deployment, environment configurations, container orchestration, etc.

ChatGPT assists DevSecOps by providing natural language features to reduce human errors:

Secret management - ChatGPT can automate the retrieval and injection of secrets using secret managers like Onboardbase. Given an environment configuration and a variable name, it’s easy for a language model to map the proper environment variables together at run time. They can also use encryption and secure communication protocols to prevent MITM attacks and ensure that secrets are manipulated securely.
Secure continuous integration & delivery - ChatGPT can predict resource usage and optimize scaling strategies to reduce costs and improve performance by analyzing pipeline configuration files. They can also identify potential bottlenecks in the pipeline and suggest ways to optimize the deployment process.
Machine-to-machine interfaces - ChatGPT can develop dynamic devops pipeline configurations on the spot, given a few parameters, adding a helpful abstraction layer between developers and deploy environments. Removing the need to handle sensitive information by humans is a big deal because it eliminates the most significant sources of data breaches―phishing and other social engineering attacks.

6. Password Management

Stolen credentials are the most common cause of data breaches: password management is complex due to the sheer number of passwords that need to be managed and the risk of password reuse and weak passwords.

To solve this, ChatGPT can suggest password policies that are easy to remember yet safe, as well as recommend password management best practices to store, update, and share them with colleagues securely:

Human-readable password generation - ChatGPT can generate password candidates that are both secure and easy to remember, using familiar words, numbers, and symbols while adding randomness to prevent guesses and a minimum length.
Smart password rotation - Automated password rotation can also be implemented by telling ChatGPT to analyze password usage. For example, you can detect when a user’s password has been compromised by looking up public lists or when a password has not been changed for a certain period of time.

7. Identity Management

Identity management is vital in modern software systems, from authentication to authorization and group access control. Thanks to their multi-media capabilities, AI models can contribute to strengthening identity management systems with multi-factor authentication and authorization:

Passwordless authentication - Better than passwords, image-based AI models can be trained to recognize individuals based on their unique biometric characteristics―face, fingerprints, voice―but also with complex user metadata like locations and devices. For example, a user could show their face to a camera, but what if you hold another person’s picture? You could use verified devices like your phone to prevent unauthorized access. No password means fewer data breaches and higher user activity.
Automated authorization - Same for authorization policies. ChatGPT could analyze org charts and automatically create user groups with fine-tuned granular access policies that match their workflows. Instead of having cybersecurity analyze how teams work for a month to design authorization requirements, ChatGPT can generate role configurations in minutes.

8. Compliance Management

Security compliance is essential for enterprise products or to speed up the due diligence process for an acquisition. It’s a mark of quality that increases customer trust and result in more sales.

But compliance management is complex and expensive: each standard has its own regulations, and you need a neutral third party to make the assessments in your company’s name. ChatGPT can not only explain these requirements to you in terms you can understand but also analyze your codebase to suggest changes:

Automated compliance modeling - When asked, ChatGPT understands instances where sensitive data is being accessed or transmitted without appropriate security controls or where user access privileges do not comply with regulatory requirements. It can then generate reports on compliance status, including identifying areas where compliance is not met and providing recommendations. This can help organizations quickly identify potential compliance issues and take appropriate action to address them.
Compliance review automation - Common software compliance standards like SOC-2 cost thousands of dollars to pass. Training dedicated models would help more organizations deliver secure products and help customers protect their digital assets.

Challenges, Risks & Opportunities

Risks & Challenges

AI systems have security risks that need to be addressed by cybersecurity experts: the complexity of AI systems, the large amount of data they handle, and their ability to learn and adapt can create unique security challenges.

Hackers can attack AI systems with common cyber attacks like malware, phishing, and denial-of-service attacks. For example, ChatGPT can create realistic phishing emails that trick users into clicking on harmful links or attachments.

Manipulating ChatGPT using prompt injection and adversarial machine learning to disrupt its initial purpose or cause it to produce incorrect statements is also widely documented online. You can bully AI models into saying anything, really.

On the developer side, errors and biases in the data or design of an AI model can also make it vulnerable. If these issues are not detected, they can lead to discrimination or harmful suggestions.

Opportunities

In problems lie opportunities. Cybersecurity professionals with specialized knowledge in AI security will strive: the increasing importance of AI in various industries creates a growing demand. Those who move now will reap the most significant benefits.

It’s crucial for cybersecurity experts to have a comprehensive understanding of how AI systems work to identify any potential vulnerabilities and threats. By doing so, they can develop secure AI systems, protect data privacy and integrity, and devise innovative security techniques to ward off any cyber attacks. They will still need to manage the risks of AI, such as false positives or false negatives in security alerts, and guarantee that AI systems are transparent, easy to understand, and impartial in their decision-making process.

Overall, experts must stay updated with the latest techniques to prepare for the impact of AI on cybersecurity.

Aside from AI, human error remains the leading factor of security threats―phishing attacks still represent 37% of all cyber attacks, according to Statista―so there is still plenty to do.

Conclusion

While it is natural to feel anxious about the impact of AI on job security, it is essential to understand that AI isn’t a substitute for human intelligence, adaptability, and creativity: it’s a valuable tool that can enhance human capabilities and boost job performance.

To safeguard against job loss and keep pace with the changing work landscape, it is crucial to develop uniquely human skills―critical thinking, problem-solving, creativity, and emotional intelligence. But stay informed about the latest advancements in AI to identify opportunities to leverage it to improve your performance and stay ahead of potential job disruptions.

Remember, the human brain is a remarkable organ that is more efficient than any AI system, and it will remain an invaluable asset in a world of finite resources. While AI may someday take us to new frontiers, we must act now to prepare for the future and ensure that we remain an essential workforce component.

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