Mike Bruchanski, Chief Product Officer at HiddenLayer, brings over twenty years of experience in product development and engineering to the corporate. In his role, Bruchanski is chargeable for shaping HiddenLayer’s product strategy, overseeing the event pipeline, and driving innovation to support organizations adopting generative and predictive AI.
HiddenLayer is the leading provider of security for AI. Its security platform helps enterprises safeguard the machine learning models behind their most significant products. HiddenLayer is the one company to supply turnkey security for AI that doesn’t add unnecessary complexity to models and doesn’t require access to raw data and algorithms. Founded by a team with deep roots in security and ML, HiddenLayer goals to guard enterprise AI from inference, bypass, extraction attacks, and model theft.
You’ve had a formidable profession journey across product management and AI security. What inspired you to hitch HiddenLayer, and the way does this role align along with your personal and skilled goals?
I’ve all the time been drawn to solving latest and complicated problems, particularly where cutting-edge technology meets practical application. Over the course of my profession, which has spanned aerospace, cybersecurity, and industrial automation, I’ve had the chance to pioneer modern uses of AI and navigate the unique challenges that include it.
At HiddenLayer, those two worlds—AI innovation and security—intersect in a way that’s each critical and exciting. I recognized that AI’s potential is transformative, but its vulnerabilities are sometimes underestimated. At HiddenLayer, I’m capable of leverage my expertise to guard this technology while enabling organizations to deploy it confidently and responsibly. It’s the proper alignment of my technical background and fervour for driving impactful, scalable solutions.
What are probably the most significant adversarial threats targeting AI systems today, and the way can organizations proactively mitigate these risks?
The rapid adoption of AI across industries has created latest opportunities for cyber threats, very like we saw with the rise of connected devices. A few of these threats include model theft and inversion attacks, wherein attackers extract sensitive information or reverse-engineer AI models, potentially exposing proprietary data or mental property.
To proactively address these risks, organizations must embed security at every stage of the AI lifecycle. This includes ensuring data integrity, safeguarding models against exploitation, and adopting solutions that give attention to protecting AI systems without undermining their functionality or performance. Security must evolve alongside AI, and proactive measures today are the perfect defense against tomorrow’s threats.
How does HiddenLayer’s approach to AI security differ from traditional cybersecurity methods, and why is it particularly effective for generative AI models?
Traditional cybersecurity methods focus totally on securing networks and endpoints. HiddenLayer, nonetheless, takes a model-centric approach, recognizing that AI systems themselves represent a novel and helpful attack surface. Unlike conventional approaches, HiddenLayer secures AI models directly, addressing vulnerabilities like model inversion, data poisoning, and adversarial manipulation. This targeted protection ensures that the core asset—the AI itself—is safeguarded.
Moreover, HiddenLayer designs solutions tailored to real-world challenges. Our lightweight, non-invasive technology integrates seamlessly into existing workflows, ensuring models remain protected without compromising their performance. This approach is especially effective for generative AI models, which face heightened risks resembling data leakage or unauthorized manipulation. By specializing in the AI itself, HiddenLayer sets a brand new standard for securing the longer term of machine learning.
What are the largest challenges organizations face when integrating AI security into their existing cybersecurity infrastructure?
Organizations face several significant challenges when attempting to integrate AI security into their existing frameworks. First, many organizations struggle with a knowledge gap, as understanding the complexities of AI systems and their vulnerabilities requires specialized expertise that isn’t all the time available in-house. Second, there is usually pressure to adopt AI quickly to stay competitive, but rushing to deploy solutions without proper security measures can result in long-term vulnerabilities. Finally, balancing the necessity for robust security with maintaining model performance is a fragile challenge. Organizations must make sure that any security measures they implement don’t negatively impact the functionality or accuracy of their AI systems.
To deal with these challenges, organizations need a mixture of education, strategic planning, and access to specialized tools. HiddenLayer provides solutions that seamlessly integrate security into the AI lifecycle, enabling organizations to give attention to innovation without exposing themselves to unnecessary risk.
How does HiddenLayer ensure its solutions remain lightweight and non-invasive while providing robust security for AI models?
Our design philosophy prioritizes each effectiveness and operational simplicity. HiddenLayer’s solutions are API-driven, allowing for straightforward integration into existing AI workflows without significant disruption. We give attention to monitoring and protecting AI models in real time, avoiding alterations to their structure or performance.
Moreover, our technology is designed to be efficient and scalable, functioning seamlessly across diverse environments, whether on-premises, within the cloud, or in hybrid setups. By adhering to those principles, we make sure that our customers can safeguard their AI systems without adding unnecessary complexity to their operations.
How does HiddenLayer’s Automated Red Teaming solution streamline vulnerability testing for AI systems, and what industries have benefited most from this?
HiddenLayer’s Automated Red Teaming leverages advanced techniques to simulate real-world adversarial attacks on AI systems. This allows organizations to:
- Discover vulnerabilities early: By understanding how attackers might goal their models, organizations can address weaknesses before they’re exploited.
- Speed up testing cycles: Automation reduces the time and resources needed for comprehensive security assessments.
- Adapt to evolving threats: Our solution repeatedly updates to account for emerging attack vectors.
Industries like finance, healthcare, manufacturing, defense, and important infrastructure—where AI models handle sensitive data or drive essential operations—have seen the best advantages. These sectors demand robust security without sacrificing reliability, making HiddenLayer’s approach particularly impactful.
As Chief Product Officer, how do you foster a data-driven culture in your product teams, and the way does that translate to higher security solutions for patrons?
At HiddenLayer, our product philosophy is rooted in three pillars:
- Final result-oriented development: We start with the tip goal in mind, ensuring that our products deliver tangible value for patrons.
- Data-driven decision-making: Emotions and opinions often run high in startup environments. To chop through the noise, we depend on empirical evidence to guide our decisions, tracking the whole lot from product performance to market success.
- Holistic pondering: We encourage teams to view the product lifecycle as a system, considering the whole lot from development to marketing and sales.
By embedding these principles, we’ve created a culture that prioritizes relevance, effectiveness, and adaptableness. This not only improves our product offerings but ensures we’re consistently addressing the real-world security challenges our customers face.
What advice would you give organizations hesitant to adopt AI because of security concerns?
For organizations wary of adopting AI because of security concerns, it’s essential to take a strategic and measured approach. Begin by constructing a powerful foundation of secure data pipelines and robust governance practices to make sure data integrity and privacy. Start small, piloting AI in specific, controlled use cases where it may well deliver measurable value without exposing critical systems. Leverage the expertise of trusted partners to handle AI-specific security needs and bridge internal knowledge gaps. Finally, balance innovation with caution by thoughtfully deploying AI to reap its advantages while managing potential risks effectively. With the best preparation, organizations can confidently embrace AI without compromising security.
How does the recent U.S. Executive Order on AI Safety and the EU AI Act influence HiddenLayer’s strategies and product offerings?
Recent regulations just like the EU AI Act highlight the growing emphasis on responsible AI deployment. At HiddenLayer, we now have proactively aligned our solutions to support compliance with these evolving standards. Our tools enable organizations to exhibit adherence to AI safety requirements through comprehensive monitoring and reporting.
We also actively collaborate with regulatory bodies to shape industry standards and address the unique risks related to AI. By staying ahead of regulatory trends, we ensure our customers can innovate responsibly and remain compliant in an increasingly complex landscape.
What gaps in the present AI security landscape must be addressed urgently, and the way does HiddenLayer plan to tackle these?
The AI security landscape faces two urgent gaps. First, AI models are helpful assets that must be protected against theft, reverse engineering, and manipulation. HiddenLayer is leading efforts to secure models against these threats through modern solutions. Second, traditional security tools are sometimes ill-equipped to handle AI-specific vulnerabilities, making a need for specialised threat detection capabilities.
To deal with these challenges, HiddenLayer combines cutting-edge research with continuous product evolution and market education. By specializing in model protection and tailored threat detection, we aim to supply organizations with the tools they should deploy AI securely and confidently.
