Erdinç Balcı discusses cybersecurity strategies in the age of artificial intelligence, emphasizing how AI-driven approaches should evolve to improve many ecosystems. With over 20 years of experience in the cybersecurity industry, Balcı has built a career that bridges the structured world of banking and the dynamic realm of modern cybersecurity. Starting as an information security consultant and later serving as a specialist at one of Turkey’s largest financial institutions, he gained a deep understanding of the importance of robust security frameworks in highly regulated environments. His career trajectory took him to senior management roles in international cybersecurity firms, where he managed global-scale projects and led initiatives that shaped the industry’s evolution. Today, as the founder of Cerebro Cyber Security, Balcı continues to pioneer innovative, AI-powered solutions to combat modern digital threats, leveraging his expertise to help organizations stay ahead of ever-evolving risks.
AI and cybersecurity—two words that evoke both opportunity and urgency in today’s digital age. As cyber threats become increasingly sophisticated, the integration of artificial intelligence into cybersecurity strategies is no longer optional; it’s a necessity. From detecting subtle anomalies to thwarting large-scale attacks, AI is rewriting the rules of defense.
It’s a long way from the structured world of banking to the ever-evolving battlefield of cybersecurity, yet some professionals have bridged this gap with striking success. With over a decade of experience in information security, this journey showcases how expertise in regulated industries can fuel innovation in AI-driven defense strategies.
The rise of AI in cybersecurity
Cybersecurity has always been a game of cat and mouse. Traditional defenses relied heavily on human vigilance—manual monitoring of logs, static firewalls, and reactionary responses to incidents. While these approaches served well in an era of predictable threats, the rapid evolution of technology has introduced complexities that far outpace human scalability. Phishing schemes grow more sophisticated, ransomware attacks target critical infrastructures, and adversaries find new ways to exploit vulnerabilities faster than ever before. The need for something smarter, faster, and more adaptable is clear.
Artificial intelligence, with its ability to process massive amounts of data in real-time, doesn’t just react to threats—it predicts and prevents them. AI systems identify anomalies and risks by automating threat detection that would otherwise go unnoticed in the chaos of sprawling networks. For example, AI can detect subtle patterns in network traffic to signal a breach before it escalates or identify malicious payloads buried in innocuous-looking emails.
What makes AI truly transformational is its adaptability. Traditional systems are rigid—if the parameters aren’t explicitly defined, they fail. AI, however, learns and evolves. Machine learning models adapt to new threats, ensuring defenses stay one step ahead of attackers. The ability to perform tasks like natural language processing (NLP) has even opened doors to understanding adversarial prompts in ways no static algorithm could.
AI systems are impressive in their capabilities, but they’re far from invincible. Testing these systems for weaknesses has proven critical to ensuring their safety and reliability. Through strategic probing—red teaming—key lessons emerge about how AI systems interact with the real world, revealing flaws that could lead to catastrophic consequences if left unchecked.
Probing AI systems for flaws
Understanding how an AI system is applied and where it could fail is the first step in uncovering vulnerabilities. Systems designed for tasks like generating text or images often face risks far beyond their intended applications. For instance, text-to-image generators have demonstrated biases when handling ambiguous prompts. Without explicit input, these systems may produce outputs that perpetuate stereotypes or amplify harmful assumptions. This highlights the importance of anticipating not just what an AI system can do, but what unintended consequences might arise in its deployment.
AI vulnerabilities
Not all vulnerabilities require sophisticated attacks to exploit. Simple, well-crafted adversarial inputs can bypass even the most advanced safety measures. A classic example is phishing attempts that exploit AI’s ability to follow instructions. By crafting prompts that subtly nudge the system toward harmful behavior, attackers can evade traditional safeguards. The accessibility of such methods underscores the need to address risks at every level of a system—its core design, interfaces, and real-world usage.
Automation further broadens the scope of risk analysis. By automating attacks, testers can uncover a wider array of vulnerabilities across diverse use cases and contexts. Whether it’s simulating phishing schemes, exploring edge-case inputs, or identifying data leakage points, automation accelerates the discovery process, ensuring no stone is left unturned.
The human element
While automation is a powerful tool, human creativity remains irreplaceable. Red teaming thrives on the ability to think like an adversary, and this often requires a nuanced understanding of culture, context, and intent. For example, certain vulnerabilities, such as a system’s response to emotionally charged prompts, are best identified by experienced testers who can evaluate the ethical and social implications of AI outputs. Human judgment ensures that red teaming moves beyond mere technical assessment, probing into how systems might behave in complex, real-world scenarios.
Ultimately, red teaming is as much an art as it is a science.
Transitioning from banking to cybersecurity
The leap from banking to cybersecurity might seem like crossing a chasm, but the parallels between these two worlds are striking. Both operate in high-stakes environments where the cost of failure is immense, and both demand a proactive, meticulous approach to threats. Lessons learned from securing financial systems have seamlessly informed strategies for defending against today’s AI-driven risks.
What banking taught about security
The financial sector thrives on trust. From safeguarding sensitive customer data to preventing fraudulent transactions, banking’s security protocols are designed to withstand relentless attempts to exploit vulnerabilities. This experience is directly transferable to cybersecurity, where protecting sensitive information remains a cornerstone of every strategy.
Take financial fraud detection as an example. Banks have long relied on pattern recognition to detect anomalies, whether it’s identifying unusual transaction activity or uncovering counterfeit documents. The same principles are now amplified by AI in cybersecurity. Adaptive algorithms, akin to those used in fraud detection, analyze behavior and patterns in real-time to identify potential threats, making responses faster and more precise.
New challenges in cybersecurity
While banking taught the importance of vigilance, cybersecurity comes with its own set of challenges, particularly when AI systems are in the mix. AI introduces risks that traditional systems never had to consider. For example, data exfiltration—unauthorized data transfer—can occur silently in systems that rely heavily on AI for operational efficiency. Similarly, system-level vulnerabilities, such as insecure APIs or insufficient prompt validation, create new attack surfaces for adversaries.
A particularly alarming case involves the use of large language models (LLMs) to automate scams. By coupling LLMs with speech synthesis technologies, attackers can create end-to-end systems designed to deceive users convincingly. Imagine a scammer using AI to generate a personalized voice message that mimics a trusted source, complete with an emotional tone and persuasive arguments. The risks extend beyond traditional phishing; they reach a level of sophistication that exploits human trust on a deeply psychological level.
AI’s role in next-gen security strategies
The nature of cybersecurity has always been about staying one step ahead of the attacker, but AI has fundamentally changed the rules of engagement. Gone are the days when static defenses—fixed rules and rigid systems—were enough to secure a network. Today, adaptive, AI-powered systems are the frontline of defense, learning and evolving alongside the threats they aim to counter.
From static defenses to dynamic learning systems
Traditional security relied heavily on predefined rules: if a known malicious signature was detected, action was taken. This reactive approach worked well in a time when threats evolved slowly. But attackers today are faster, more innovative, and often unpredictable. Static defenses are simply no match for this new reality.
AI offers a dynamic alternative. Adaptive models continuously learn from data, identifying patterns that humans would miss. These systems can spot anomalies in real-time, flagging unusual behaviors that might indicate a breach. What makes this shift revolutionary is that AI doesn’t just follow rules; it rewrites them as new data emerges. This adaptability ensures that defenses remain effective even as attack methods evolve.
Redefining risk with generative AI
Generative AI has added both potential and peril to the cybersecurity landscape. On the one hand, it powers advanced tools that can simulate and anticipate attacks, providing security teams with invaluable insights. On the other, it introduces novel harm categories that didn’t exist a few years ago.
For instance, misinformation campaigns driven by AI-generated content can spread false narratives with unprecedented speed and scale. Similarly, adversarial misuse of AI tools—such as crafting fake identities or generating malicious code—poses significant challenges. These risks require a redefinition of what it means to secure a system. It’s no longer just about protecting data; it’s about safeguarding the very fabric of trust that underpins digital ecosystems.
Leveraging automation for AI-driven testing
Scaling these strategies to meet the demands of modern cybersecurity wouldn’t be possible without automation. Open-source frameworks have emerged as invaluable tools for red-teaming operations, automating the testing of AI systems at unprecedented scales. These frameworks enable security teams to simulate diverse attack scenarios, from adversarial prompts to data leakage experiments, without the need for extensive manual effort.
Automated tools don’t replace human ingenuity—they amplify it. By handling repetitive tasks, these systems free up experts to focus on creative problem-solving, ensuring that the ever-changing security landscape is met with resilience and precision.
Future-proofing AI security
As threats evolve, so must the strategies to counter them. The static, “set-it-and-forget-it” security models of the past are obsolete in the face of today’s dynamic risks. Future-proofing cybersecurity isn’t about achieving perfection—it’s about building systems that are resilient, adaptive, and continually improving.
Break-fix cycles and continuous evolution
The concept of a break-fix cycle—repeatedly testing, identifying vulnerabilities, and fixing them—is becoming a cornerstone of modern AI security. Red teaming plays a crucial role in this iterative process. By continually probing systems for weaknesses, organizations ensure that their defenses aren’t just keeping up with threats but anticipating them.
However, this relentless pace of evolution requires a delicate balance with regulation. While innovation drives progress, over-regulation can stifle it. Effective cybersecurity strategies need to walk the fine line between advancing technology and adhering to frameworks that ensure safety without hindering innovation. This balance is key to fostering systems that are both secure and sustainable.
Cross-industry collaboration
One of the most valuable lessons from industries like banking is the emphasis on collaboration. Just as financial institutions pool resources and intelligence to combat fraud, cybersecurity can benefit from similar collective efforts. Banking’s meticulous attention to trust, accountability, and risk management provides a blueprint for enhancing cybersecurity practices.
Diverse perspectives are another essential element in future-proofing AI security. AI systems operate across industries, geographies, and cultures. Incorporating expertise from different sectors ensures that defenses are robust against a wide range of threats. Whether it’s lessons from regulated industries or insights from ethical AI researchers, collaboration strengthens the foundation of security.
The last stop: AI’s role in safeguarding the future
The time for action is now. As cyber threats grow more sophisticated, industries across the board must embrace AI-driven strategies to secure their systems. Waiting for threats to materialize is no longer an option—proactivity is the only way forward. Collaboration, innovation, and a commitment to ethical security are the pillars on which this future will be built.
- Adopt a continuous testing mindset. Security is not a one-and-done exercise. Implement iterative testing strategies, such as red teaming and break-fix cycles, to ensure your defenses evolve in tandem with emerging threats. Regularly probing for vulnerabilities and fixing them isn’t just proactive—it’s essential for resilience.
- Balance innovation with regulation. Too much regulation can choke innovation, but a lack of oversight can lead to chaos. Find the sweet spot where technological advancements thrive without compromising safety or ethical standards. Collaborate with regulatory bodies to create frameworks that encourage innovation while setting guardrails against misuse.
- Leverage cross-industry knowledge. Don’t limit cybersecurity strategies to lessons learned within the tech space. Banking, healthcare, and even logistics offer insights into managing trust, accountability, and risk at scale. Borrowing from these sectors ensures a multi-faceted approach to securing AI systems.
- Automate, but don’t over-rely on machines. Automation is a game-changer for scalability, but it’s not a substitute for human intuition and creativity. Use automation to handle repetitive tasks and surface insights, but keep human experts at the helm to analyze complex risks and ethical implications that machines can’t fully understand.
- Prioritize ethical alignment. AI security isn’t just about technical robustness—it’s about trust. Ensure that your AI systems are designed not only to be secure but also to align with ethical principles. Build transparency into your processes, foster accountability, and develop tools that actively prevent misuse or harm.
