The Security Strategist Podcast: Neural Networks at Risk
Discover the latest insights on AI security with Dr. Peter Garraghan, CEO of Mindgard, in this podcast episode. Learn about the security threats that neural networks face.
Fergal Glynn
Artificial intelligence (AI) is transforming nearly every corner of cybersecurity—and offensive security (OffSec) operations are no exception.
Offensive security was once a field dominated by manual penetration tests and scripted simulations. But today, OffSec tools are now evolving with AI-driven capabilities that can adapt, learn, and scale at speeds that human teams simply can’t match.
However, like any innovation, AI’s role in offensive security presents both immense potential and significant challenges. Organizations that embrace AI without understanding its complexities could find themselves facing new vulnerabilities instead of solving old ones.
In this guide, you’ll learn the upsides and challenges of using AI for offensive security operations, as well as how offensive security platforms like Mindgard help businesses navigate the new frontier of always-on cybersecurity.
AI is transforming OffSec for the better, providing security teams with an unprecedented edge over attackers. Used strategically, AI has the potential to make OffSec more thorough and effective.
AI systems can process vast amounts of data and execute simulated attacks much faster than human teams alone. This speed allows organizations to test more scenarios in less time, scale penetration tests across multiple environments, and keep pace with evolving threat landscapes.
Not only can AI conduct these attacks more quickly, but it can also operate nonstop. Because AI can operate around the clock, organizations can shift toward continuous security validation rather than periodic pentesting.
This approach means catching new vulnerabilities as they emerge, not months later.
AI can quickly identify patterns and anomalies in networks, applications, and systems that might otherwise go unnoticed. By simulating real-world adversarial behavior based on these insights, OffSec teams can create more dynamic, realistic attack models—and uncover vulnerabilities before malicious actors do.
AI-driven tools can also automate the scanning, categorization, and prioritization of vulnerabilities based on real-world exploitability, rather than relying solely on static severity scores. This helps OffSec teams focus their energy where it matters most, especially if there are multiple issues to address.
Unlike static scripts or traditional red team exercises, AI systems can adapt mid-simulation. They can alter attack paths based on network defenses, user behavior, and environmental changes, providing a more comprehensive and authentic view of an organization’s security posture.
Learn more about how modern red team offensive security practices are evolving alongside AI.
AI’s biggest selling point is its ability to take time-consuming, manual tasks off your team’s plate. AI can handle many of the time-consuming tasks involved in offensive security, like reconnaissance, password cracking, or generating phishing payloads.
In this way, AI frees human operators to focus on strategic analysis, critical thinking, and creative adversarial testing.
AI is incredibly valuable for cybersecurity operations, but it isn’t perfect. Organizations must effectively navigate these challenges in using AI for offensive security operations.
AI models are only as good as the data they're trained on. If training data is incomplete, outdated, or biased, AI systems can misidentify threats, overlook vulnerabilities, or simulate unrealistic attack paths.
Offensive security teams must source diverse, up-to-date datasets and continuously monitor AI outputs for accuracy and fairness.
Ironically, AI itself can become a target. Attackers may use adversarial inputs—carefully crafted data designed to trick AI systems—causing the models to miss threats or make wrong decisions during testing.
Securing AI models against manipulation is becoming as critical as securing the systems they’re tasked to assess. Solutions like Mindgard allow security teams to assess the safety of their internal AI models, ensuring end-to-end security.
Using AI for offensive purposes raises important ethical and legal questions. How do you ensure that automated tools don’t cross the line into unauthorized testing? How do you protect sensitive training data?
Your organization must establish robust ethical frameworks and governance policies to effectively guide AI-driven OffSec activities.
Artificial intelligence is redefining what’s possible in business. Used thoughtfully, it has a lot of potential for offensive security, improving everything from accuracy to the quality of attack simulations.
However, AI itself can become a threat vector, so organizations must account for malicious attacks in their large language models.
Mindgard’s purpose-built Offensive Security platform helps organizations safely deploy AI for offensive security and virtually any other use. Mindgard offers continuous, AI-driven security validation while keeping transparency, ethics, and human collaboration at the core of our methodology. Future-proof your OffSec: Get a Mindgard demo now.
No. While AI can automate and enhance many aspects of offensive security operations, you can’t replace human expertise.
Humans bring creativity, intuition, and strategic thinking that AI can’t replicate. You can achieve the best results by combining AI-driven automation with the expertise of skilled security professionals.
If you’re looking to build human expertise, Mindgard highlights the top offensive security certifications and training programs to strengthen your team’s skills.
Frequently. Threats evolve rapidly, and static models can quickly become outdated.
Organizations should regularly retrain or fine-tune their AI models—ideally on a monthly or quarterly basis—and continuously update them with the latest threat intelligence and real-world data.
Begin by identifying specific pain points where AI could add value, such as automating reconnaissance or continuous testing, and then pilot AI tools in a controlled environment. Opting for a solution like Mindgard can also accelerate adoption with built-in safeguards and best practices.
