How AI is Helping Our Offensive Security Testing

The integration of artificial intelligence into offensive security practices represents a significant evolution in how cybersecurity assessments are conducted. Last year, we wrote about how AI wouldn't replace us human testers at SilentGrid Security, this year, we're taking a look at how AI has helped us improve our effectiveness. We have carefully incorporated AI capabilities into our workflow over the past year, yielding measurable improvements in efficiency and output quality. This article examines the practical applications we have implemented, the challenges we have navigated, and the genuine value these technologies provide to our clients.

Accelerating Custom Tool Development

One of the most substantial benefits we have observed is the reduction in time required to develop bespoke testing tools. Previously, the overhead associated with creating custom code to examine specific vulnerabilities often meant that certain issues could only be documented theoretically rather than demonstrated practically.

A recent engagement illustrates this point clearly. During testing, we identified that a client had implemented proprietary encryption for data transmission. In previous years, we would have noted the concern that custom cryptographic implementations frequently contain flaws, but lacked the time within the engagement to build the tooling necessary to validate this assertion thoroughly.

With AI-assisted development, we were able to construct a complete decryption routine while simultaneously progressing with other testing activities. The result was a working demonstration that decrypted data captured from the network, providing the client with concrete evidence rather than theoretical risk. This tangible proof significantly enhanced the client's understanding of the issue and its potential business impact.

Generating Realistic Test Data

Testing applications thoroughly often requires substantial volumes of realistic data. Creating information that appears authentic without using actual sensitive data has historically been time-consuming and prone to patterns that might not adequately stress-test an application.

AI systems excel at generating diverse, realistic datasets for testing purposes. Whether populating forms during application testing or creating files for upload to database systems, these tools can produce varied content that more accurately simulates real-world usage patterns.

There are, however, practical limitations. When testing systems that process sensitive information such as medical records or financial data, AI systems often activate refusal mechanisms that prevent generation of such content. While these safeguards exist for valid reasons, they do present obstacles that must be worked around during certain types of security assessments.

Source Code Review and Reverse Engineering

AI-assisted analysis has proven particularly valuable when reviewing source code and reverse engineering compiled applications. These systems demonstrate strong capability in identifying code sections that perform security-relevant functions and evaluating whether they adhere to established best practices.

For instance, when examining encryption implementations, AI tools can rapidly locate the relevant functions and assess whether key lengths, algorithm choices, and implementation patterns align with current standards. This accelerates the initial review phase and allows security consultants to focus their expertise on more nuanced analysis.

It is important to note that current AI capabilities have limitations. Complex vulnerabilities that arise from chains of individually low-risk issues, or those requiring deep understanding of business logic, remain firmly within the domain of human expertise. AI serves as an accelerant for certain tasks rather than a replacement for skilled analysis.

Maintaining and Modernising Existing Tools

The offensive security field relies heavily on specialised tooling, much of which is developed for specific purposes and then archived. We have found AI assistance valuable in two related areas: updating our own legacy tools and repairing external tools that no longer function.

Tools developed years ago for particular engagements often have limited usability. They may contain hard-coded parameters, lack command-line interfaces, or provide minimal user feedback. Modernising these tools manually would be time-intensive, often discouraging reuse. AI-assisted development makes it practical to enhance these tools with proper parameter handling, improved interfaces, and better documentation, transforming single-purpose utilities into reusable assets.

Similarly, we frequently encounter external tools that would be ideal for specific testing scenarios but fail to execute due to deprecated dependencies, outdated APIs, or incompatibilities with modern systems. Resolving these issues traditionally required substantial effort, particularly with tools written in unfamiliar languages or using obsolete technologies. AI assistance has significantly reduced the time required to update and repair these tools, expanding the practical toolset available during engagements.

Enhancing Report Quality and Consistency

Client reporting has benefited considerably from AI integration, though our approach here has been deliberately measured. With multiple consultants contributing to reports, maintaining consistent tone, terminology, and structure presents an ongoing challenge. Individual writing styles, while each professionally acceptable, can create an inconsistent reading experience across a comprehensive assessment report.

Through an iterative development process, we have implemented tools that assist with report preparation while maintaining authenticity. Importantly, we have avoided using AI to generate report content directly. Instead, these tools summarise consultant findings or suggest alternative phrasings for consultant-authored content. This approach preserves the technical accuracy and professional judgement that can only come from the consultants who performed the work, while improving consistency in presentation.

Addressing Data Sovereignty Concerns

The implementation of AI capabilities within a security consultancy presents unique challenges, particularly regarding data handling. Client information is invariably sensitive, and our professional obligations require that we maintain strict controls over where and how this data is processed.

A primary concern was ensuring that interactions with AI systems would not result in client data being incorporated into training datasets or otherwise exposed. We approached integration cautiously, carefully evaluating the data handling practices of any platforms under consideration. Where necessary, we implemented on-premises solutions or worked with providers offering contractual guarantees regarding data usage and isolation.

This careful approach necessarily slowed our adoption of AI capabilities, but was essential to maintain the trust our clients place in us when providing access to their systems and data.

The Human Element

While this article has focused on practical applications of AI in offensive security testing, it would be incomplete without acknowledging that potentially the most frequent use of these technologies within our team has been entirely non-technical. Given that our consultants often work remotely (from locations ranging from home offices to coffee shops to the beach), team communication occurs primarily through digital channels.

The creation and sharing of humorous content has become a notable use case for AI tools within our internal communications. Whether generating images of security vulnerabilities depicted as increasingly worried-looking cartoon characters, or creating variations on classic memes adapted to extremely specific cybersecurity scenarios, these applications have seen more use than any of us initially predicted.

This lighter application, while not directly related to service delivery, contributes to team cohesion and morale. A team that laughs together at AI-generated images of anthropomorphised SQL injection attacks is a team that works well together. This indirectly supports the quality of work we provide to clients, though we have elected not to include "meme generation capabilities" in our service descriptions.

Conclusion

The integration of AI capabilities into offensive security practices has yielded tangible benefits in terms of efficiency and output quality. At SilentGrid Security, we have found these technologies most valuable as accelerants for specific tasks: developing custom tools, generating test data, reviewing code, and maintaining consistency in reporting.

These improvements translate to client value through more thorough testing within engagement timeframes, better demonstration of identified issues, and more consistent reporting. However, the fundamental work of security assessment remains rooted in human expertise, professional judgement, and creative problem-solving.

AI serves as a capable assistant for certain tasks, but the complex, contextual analysis required for comprehensive security assessment continues to require experienced security professionals. Our measured approach to AI integration reflects this reality: embracing efficiency gains where appropriate while maintaining the rigorous human analysis that effective security testing demands.