How Will AI Affect the Cybersecurity Field? Experts Share Their Predictions

Artificial Intelligence (AI) is revolutionizing multiple industries, and cybersecurity is no exception. As the digital landscape expands, so do the threats from cybercriminals, who are now leveraging AI to enhance their attacks. At the same time, AI-driven solutions are being deployed by security professionals to protect critical systems and data. The ongoing battle between attackers and defenders in cyberspace has become more complex, with AI as the new battlefield.

A particularly egregious example of AI’s dual role in cybersecurity was highlighted in a 2024 case where fraudsters used deepfake technology to swindle $25 million from a Hong Kong-based company. The criminals impersonated the CEO through an AI-generated deepfake video, deceiving an accountant into transferring the funds. This incident showcased the alarming potential of AI-powered attacks, and also raised questions about how could AI can be used in defense in order to effectively prevent disasters like this.

AI’s role in cybersecurity is evolving rapidly, and experts have varied perspectives on its effectiveness, benefits, risks, and limitations. On one hand, AI offers transformative benefits such as automating tasks, detecting new and emerging threats in real time, and even predicting potential vulnerabilities. On the other hand, it presents new challenges, particularly in the hands of adversarial actors who can exploit AI for malicious purposes, from deepfakes and phishing to automated ransomware attacks.

As soon as we set out to research this topic, we realized its that its immense complexity and interdisciplinary features could easily fill an entre tome or even a series of books. However, as we know our readers are busy and would prefer to keep up with the most relevant developments in this field, we instead opted to pick the brains of some of the most talented people in the cybersecurity industry.

Below, you will find some opinions from several industry leaders, all of whom are facing the various aspects of this struggle on a daily basis in their respective fields:

How do you think AI will change the landscape of cybersecurity in the next 5 to 10 years?

Nick Quirk, COO/CTO at SEO Locale:

AI will be able to provide real time threat analysis by picking up patterns, anomalies and zero day threats. I believe AI will allow systems to get smarter and as it deals with situations it can only improve over time. Saving time on dealing with security threats and almost instantly.

On the other side hackers and anyone trying to penetrate will be able to use AI to their advantage and can create more sophisticated attacks in the future.

Will AI help the offense or defense in cyber security or would it be an equal battle. That is the real problem and nobody will know until it becomes a reality.

Aaron Shaha, Chief of Threat Research and Intelligence, at Blackpoint Cyber:

“Predicting the trajectory of AI two years ago was challenging, let alone forecasting five to ten years ahead. However, based on current trends, I believe we’ll see significant automation of routine tasks for cybersecurity defenders. This will free up time for Tier 1 SOC employees to focus on more complex triage efforts.

On the other hand, we’re already encountering entropy and decay within large language models (LLMs), as open-source ingestion has reached saturation. Hallucinations and quality issues remain prominent challenges. This decay may worsen due to feedback loops inherent in these models, leading to further degradation in their utility.

Meanwhile, offensive operators are beginning to leverage AI-powered machine learning (ML) for tasks such as analyzing vast amounts of breach data, scanning networks, and penetration testing to uncover vulnerabilities. Both attackers and defenders are also starting to use AI to identify vulnerabilities in code—a trend that is likely to accelerate. However, this too has its downsides, as some open-source projects report being inundated with low-quality bug reports generated by AI tools.”

Ashley Nuckols, CISO at Formstack:

“Cybersecurity teams, offensive or defensive, SOCs, IT department, etc, are expected to do more, and in 5 to 10 years, AI will be a critical pillar in supporting teams in exactly this. I recall a statistic that stated the average ticket submitted to a SOC analyst allowed them, on average, to review and respond somewhere between 10 to 25 minutes. Productivity of these teams are measured by tactical metrics both by industry standards and internal departments. I think the amount of work to be completed will increase and the time to analyze and respond will decrease to where do more, faster and correctly, with less, will not only be expected but achievable. And the time being spent by a human triaging will be much less, allowing them to quickly dive into the real issue and act quickly.”

In what ways can AI enhance defensive cybersecurity measures?

Ashley Nuckols, CISO at Formstack:

“Many people have stated this thought before but I believe that it is very much a positive across the board; AI allows limited defensive capabilities to expand and function as a force multiplier for teams that may lack the man power and time resources. Being able to farm out some of the defensive abilities and increase defensive coverage when resources are limited, especially as a company’s attack surface expands, is one of the mainstays of AI in cyber defense.”

Patricia Thaine, CEO & Founder at Private AI:

“AI can enhance defensive cybersecurity measures in several transformative ways. First, it dramatically improves threat detection by analyzing patterns across massive datasets to identify anomalies that human analysts might miss. AI systems can continuously monitor network traffic, user behavior, and system activities in real-time, flagging suspicious activities before they escalate into breaches. Second, AI enables adaptive authentication systems that go beyond passwords by analyzing behavioral biometrics and contextual factors to verify identities. Third, AI automates incident response, allowing security teams to focus on complex strategic issues rather than routine alerts.

Most critically, AI helps organizations tackle the “dark data” problem by extending governance to unstructured data—identifying sensitive information in documents, emails, and chat messages that would otherwise remain invisible to security systems. As these capabilities mature, we’ll see AI move from supporting human security teams to functioning as an autonomous defensive layer that can identify vulnerabilities and deploy countermeasures at machine speed, fundamentally changing how we approach cybersecurity architecture.

How effective is AI in detecting and responding to cyber threats compared to traditional methods?

Lars Koudal, CEO of WP Security Ninja:

“AI has changed the game in threat detection and response. Traditional methods often fail against the evolving tactics of cybercriminals. AI can analyze huge amounts of data, spot patterns, and detect anomalies in real time.

At WP Security Ninja, we focus on finding security weaknesses and protecting sites from attacks. While we haven’t fully used AI yet, its potential is huge. AI can spot new threats that don’t follow usual patterns, like zero-day attacks. It can also make responses faster, suggesting or automating fixes before a human can.

The big difference is that AI learns and adapts. It gets better over time, making it more effective against changing threats.”

Ashley Nuckols, CISO at Formstack:

“I think with the power of both ML and advancements in AI, the overall effectiveness in detection and response may remain similar or slightly increase compared to traditional methods, but, the speed at which things can be detected and the general improvements in learning/tweaking within the algorithm will happen much more quickly. Less time will be needed to quickly understand characteristics of new malicious behavior and more. However, I also think potential bias within training data or bad data could greatly influence the efficacy of AI threat detection and response and will be something that we will need to watch.”

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Bri Frost, Director of Security and IT Ops Curriculum at Pluralsight:

“At its core foundation, Security has been and always will be a cat and mouse game. Cyber defenders improve their security controls, alerts and detections until, eventually, attackers find another way around them. As technology gets more and more complicated, that allows for more and more entry points that can be left vulnerable. So do I see a future where AI is detecting and mitigating cyber threats? Not truly. I think there will be a lot of vendor tools and platforms that boast AI-powered detections, but that’s an ineffective technique to rely on our organizations’ security. The way these AI-defense tools will be trained to identify attacks is basic measures. Attackers already have this information and are constantly finding ways to evade these defense techniques. They will continue to do so, whether it’s done via traditional analysts or LLM-trained AI models.”

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Aayush Kamora, Head of Marketing at Auraya Systems:

“AI has proven to be significantly more effective than traditional methods in detecting and responding to cyber threats, particularly in identifying advanced tactics like synthetic voices and deepfakes. Auraya’s patented technology excels in detecting synthetic speech, which is becoming an increasingly prevalent tool for fraud and impersonation. By continuously training our voice biometric engine, ArmorVox, against various synthetic speech generators, we enhance its ability to detect and prevent malicious attempts in real-time, ensuring robust protection against the growing threats of synthetic voices and deepfake technology.”

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What are the most significant benefits of automating security tasks with AI?

Lars Koudal, CEO of WP Security Ninja:

“Automation has always been key in cybersecurity, and adding AI takes it further. AI lets us automate tasks like scanning for vulnerabilities and monitoring for suspicious activity. This frees up IT and security pros to focus on more important things.

For WP Security Ninja, automation means keeping websites safe without daily manual checks. We automate scans and alerts to prevent mistakes, a big risk in cybersecurity. AI can also automate complex tasks like analyzing logs and responding to threats in real time.

AI automation is great because it scales well. It works for one site or many, providing consistent and reliable protection every time. There are a lot of possibilities for AI to be used for automating processes.”

Sashi Jeyaretnam, Senior Director of Product Management for Security Solutions at Spirent:

Using automation with AI (AIOps) will significantly reduce the cognitive overload on the DevSecOps team and help them focus on high-value activities and genuine threats.

AI can rapidly analyze large volumes of logs, alerts, and network data from various disparate tools to identify anomalies, attack patterns, correlate events, and isolate attack radius to help security teams speed up threat detection and response.

By automating repetitive tasks and data analysis, AI minimizes the risk of common human errors and increases operational efficiency, which leads to more consistent security policies and procedures.

AI holds a lot of promise regarding predictive analytics, such as detecting zero-day vulnerabilities, forecasting the occurrence of future attacks, and so forth; time will tell the effectiveness of these advanced capabilities.

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How can AI be used in threat hunting to identify and neutralize threats proactively?

Lars Koudal, CEO of WP Security Ninja:

“AI has huge potential in proactive threat hunting. Unlike old methods, AI scans for unusual patterns and behaviors in real time. It analyzes a lot of data from network traffic, system logs, and user behavior. It would be close to impossible for a human to try to program the different circumstances and if-then logic, an AI would not need anything but rough instructions.

When AI gets smarter, it will know all about past attacks and weaknesses. It will spot patterns and find problems before we do. Or, it can make us double-check things that seem off.

This proactive approach could change how organizations handle security. It makes it possible to not just react to threats but also prevent them from getting worse.”

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What are the risks associated with adversarial AI, where attackers use AI to outsmart defensive systems?

Bri Frost, Director of Security and IT Ops Curriculum at Pluralsight:

“A common term I’m seeing discussed a lot right now is AI-powered attacks. And I think we need to be careful when using this term as it tends to immediately assume that AI is going to increase the sophistication of cyber threat actors. Now AI is going to be used in phishing attacks, crafting sneaky, targeted emails and deepfake videos. AI can be used in nefarious ways to disseminate misinformation and spread fear or confusion. When it comes to advanced threat actors, or APT groups, they will likely use AI capabilities to increase the efficiency and scale of their attacks. They can send out multiple campaigns to multiple kinds of targets more effectively, use chatbots for ransomware negotiations and many more use cases.

However, AI is not increasing the sophistication of attacks, code or techniques from these types of groups. An advanced threat actor isn’t going to go to ChatGPT, or the like, and ask it to spit out exploit code. They have crafted very detailed malware that’s connected to their infrastructure which isn’t something that AI is capable of doing. Therefore using the same defensive controls and measures to effectively defend against common attackers, will also defend against any campaign that uses AI for scale.”

Aaron Shaha, Chief of Threat Research and Intelligence, at Blackpoint Cyber:

“One area I’m personally very worried about is the massive personal data breaches we have had in 2024, including Change Health. Adversaries may be able to pair much of this data using AI to create more plausible synthetic identities to gain access to sensitive systems. The sheer breadth and depth of spilled data, fused with things like previous breaches make attacks such as the alleged “North Korean Agent” being hired much more likely. With AI, these attacks could become even more sophisticated, making it harder for defenders to detect fraudulent activities. The fusion of massive datasets with advanced AI capabilities poses a serious threat to the integrity of our digital ecosystems.”

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How can AI in cybersecurity balance the need for data access with privacy concerns?

Brett Popkey, Chief Technology Officer at Helcim:

“Organizations should set clear rules on what data AI can access and how it’s handled, backed by robust governance, access controls, transparency, and monitoring to build trust and meet privacy standards. To balance data access with privacy, privacy-preserving methods can be leveraged like differential privacy, which adds noise to protect individual records, or federated learning, where models train on decentralized data without sharing it. Encryption techniques like homomorphic encryption allow computations on encrypted data, and Secure Multi-Party Computation (SMPC) can enable collaborative analysis without exposing sensitive inputs—though these approaches work best in high-security/privacy contexts where the added cost is justified.”

Patricia Thaine, CEO & Founder at Private AI:

The balance between data access and privacy isn’t an either/or proposition—it’s a fundamental design challenge that requires rethinking our approach to data governance. Traditional security models force organizations to choose between locking data away (compromising innovation) or making it widely accessible (increasing risk). Most organizations don’t actually need raw sensitive data—they need the insights and patterns within that data. Where AI truly transforms this balance is with unstructured data. While companies have built sophisticated governance for structured data in databases, they’ve remained largely blind to the 80-90% of their information that exists in documents, emails, and messages. AI can now automatically identify sensitive information in these formats, apply appropriate protections, and maintain data utility through techniques like generalization and pseudonymization.

This approach fundamentally changes the privacy-utility tradeoff. Rather than choosing between innovation and protection, organizations can simultaneously enhance both by implementing appropriate controls based on context. The end result is a data governance framework that enables AI innovation while maintaining privacy by design—creating sustainable competitive advantage in an increasingly regulated environment.

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What are the main challenges and limitations of implementing AI in cybersecurity?

Yaroslav Savenkov, CEO of ZoogVPN:

“Well, from a VPN point of view where we must focus on both cybersecurity and privacy, it’s very important to keep the balance. This is a no-brainer that the fuel of all AI tools is data. Yet, when it comes to VPNs and their use cases – we want to make sure the third parties get as little data as possible, but at the same time we all try to utilize AI in a way that benefits the privacy and security of a given customer. So, we’d say that the biggest challenge here is to make sure we don’t sacrifice users’ privacy when building cybersecurity solutions on top of AI even if it looks very attractive to use some of this data and “feed” machine learning algorithms with them.

At the same time, it creates certain limitations for us because if there is no data to exploit, there will be no result. When we discussed it with our engineers, they suggested abstracting AI from customer data and focusing on pure tech solutions, such as new encryption algorithms that will be able to adjust dynamically and self-improve over time as well as different server-side services that need to be improved for sure in all cybersecurity solutions nowadays.”

Sashi Jeyaretnam, Senior Director of Product Management for Security Solutions at Spirent:

“Much like current systems and the human factor, AI in cyber security is not infallible, AI systems could identify legitimate transactions or traffic as malicious creating false positive events, especially in the initial deployments as the AI learns the environment.

AI requires vast amounts of high-quality, categorized, label data for training. This requires significant amounts of effort, time and skilled resources to get it right. Insufficient data or relying on generic pre-trained AI models can compromise accuracy and increase false positives/negatives.

AI in cybersecurity requires more computational extensive resources which require more power potentially increasing costs and impacting security efficacy and performance

To overcome these limitations and risk and improve the efficacy and effectiveness of AI-driven security solutions and practices it is critical to incorporate testing and continuous validation into the DevSecOps or CI/CD pipeline. This enables organizations to characterize the impact of AI-driven changes on performance, user experience or security posture and avoid any unintended consequences.”

Patricia Thaine, CEO & Founder at Private AI:

“The main challenges of implementing AI in cybersecurity stem from several interconnected issues organizations frequently underestimate. First, there’s the data quality challenge—AI systems require well-structured, properly labelled datasets, yet most security data exists in silos with inconsistent formats and minimal context. Second, many AI security solutions operate as impenetrable “black boxes” where security teams can’t understand the reasoning behind alerts, creating significant trust barriers. Third, we face a profound skills gap—finding professionals who understand both cybersecurity fundamentals and AI capabilities remains exceptionally difficult. Fourth, there’s the inherent asymmetry between attackers and defenders: while defenders must protect all potential entry points, attackers need only find a single vulnerability.

Perhaps most critical is what I call the “dark data” problem—organizations have invested heavily in securing structured data in databases, but remain largely blind to the 80-90% of their data that exists in unstructured formats like documents, emails, and messages. This creates massive security blind spots where sensitive information remains unprotected. When implementing AI security solutions, organizations also struggle with technical integration challenges and defining effective human-machine collaboration models that leverage the unique strengths of both.”

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What role will human cybersecurity professionals play as AI becomes more integrated into the field?

Nick Quirk, COO/CTO at SEO Locale:

“Professionals would start to oversee the AI system. Improve the AI system with further tweaking and training. Balancing automation with human judgement. No matter what the situation is, there will always be a need for a human in cybersecurity space.”

Sashi Jeyaretnam, Senior Director of Product Management for Security Solutions at Spirent:

“Cybersecurity professionals will continue to have a strong place in overall cybersecurity efforts as AI driven security is employed more and more. Incidence response is one area where professional oversight may be needed in mission critical applications where an error or bias from AI could have negative consequences. Context and intricacies with compliance standards (GDPR, DORA, CCPA and others) will also be an area where human interaction will be an ongoing effort.

It is important to keep in mind that overreliance and complete dependence on AI can lead to degradation of human expertise over time, leaving security teams ill-equipped when the AI-systems fail or are compromised.”

Brett Popkey, Chief Technology Officer at Helcim:

“Human cybersecurity professionals will focus on strategic oversight of how AI is leveraged, ethical considerations, and interpreting AI-generated insights. They will also address complex challenges and guide AI in contexts where nuanced judgment is more critical.”

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How can AI improve identity and access management (IAM) processes?

Aayush Kamora, Head of Marketing at Auraya Systems:

“AI significantly enhances identity and access management (IAM) by providing secure, seamless, and inclusive authentication solutions. Auraya’s voice biometric technology supports IAM by enabling real-time user verification through unique vocal characteristics, eliminating the need for vulnerable passwords or PINs. Moreover, our solutions are particularly beneficial for disabled individuals, offering an accessible and non-invasive authentication method that adapts to diverse user needs. By leveraging AI, Auraya ensures IAM processes are both secure and user-friendly, empowering organizations to safeguard access while promoting inclusivity.”

Brett Popkey, Chief Technology Officer at Helcim:

“AI can enhance IAM by automating and improving anomaly detection, adapting access policies dynamically, and streamlining authentication with behavioral biometrics. This allows for reduced friction for users and a more robust defense against unauthorized access.”

Patricia Thaine, CEO & Founder at Private AI:

“AI will revolutionize access management by extending capabilities to unstructured data—the 80-90% of organizational information currently lacking proper governance. Moreover, by analyzing actual usage patterns across all data types, AI can identify excessive privileges and recommend right-sizing based on genuine business needs rather than outdated role definitions. However, these advances depend entirely on establishing robust connections between existing systems and new AI security products.”

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Can you provide examples of real-world applications where AI has successfully enhanced cybersecurity?

Aayush Kamora, Head of Marketing at Auraya Systems:

“Auraya’s AI-powered voice biometric solutions have delivered remarkable results in real-world applications. For instance, one of the largest banks in the UK has saved millions by using Auraya’s EVA Forensics to detect and prevent fraud. The system’s advanced AI capabilities ensure real-time analysis of voice data, enabling quick identification of fraudulent activities. In another instance, a major government organization in New Zealand implemented voice biometrics for secure and seamless verification of residents using telephone services. Auraya provided the voice biometric technology and high-level consulting services to address security and usability concerns. The project has been a success, with about 80% of New Zealand’s population voice-enrolled.”

What ethical considerations should be taken into account when deploying AI in cybersecurity?

Yaroslav Savenkov, CEO of ZoogVPN:

“First thing first – as a CEO of a VPN service, I believe ethical considerations in cybersecurity, especially with VPNs, are paramount. When we are talking about cybersecurity, we have to understand that user privacy is a top priority. Our users trust us with their personal data, so we must ensure it is handled securely and never logged or shared without consent. Providing cybersecurity solutions is not only about protecting people from external threats, it’s also about making sure the service they are using actually does what it tells. Even if the temptation to sacrifice some privacy is too high, we have to keep our word and make sure we do what users expect us to do – keep them secure from all parties.

Second, transparency is critical. Users should know exactly how their data is being used and protected and there should be no surprises. This is hard to prove and we used to rely on third-party reviews or independent researchers, but our team strongly believes this is where AI can step in. We can outsource all transparency concerns to AI and create some universal ways to check whether a given service is fully transparent to its users or not. Of course, the AI itself should be built with transparency and genuine commitment to the user.

Finally, trust is built on integrity—AI and automation can help secure networks, but human oversight must remain to ensure fairness, prevent false positives, and handle nuanced decisions. Last but not least, we have to ensure that no cybersecurity solutions are used for malicious purposes and we see a huge potential of AI that can regulate and decide it over humans here.”

Patricia Thaine, CEO & Founder at Private AI:

“When deploying AI in cybersecurity, we must prioritize transparency in how these systems make decisions, ensuring security teams understand not just what was flagged but why. Organizations need clear accountability frameworks defining who’s responsible when AI
systems fail or create unintended consequences. We must guard against algorithmic bias that might create security blind spots or disproportionately flag certain user behaviors as suspicious.

Privacy preservation is non-negotiable—AI security systems should follow data minimization principles, especially when monitoring employee activities or processing unstructured data that contains sensitive information. Finally, organizations need robust human oversight mechanisms ensuring AI serves as a decision support tool rather than replacing critical human judgment in high-stakes security decisions.”

Can you explain the difference between AI and machine learning in the context of cybersecurity?

Nick Quirk, COO/CTO at SEO Locale

“They are similar in ways. The huge difference between the two is AI can mimic human intelligence meanwhile machine learning is a subset of AI that can learn.

AI makes more broader decisions for example it can activate security protocols across an entire network.

Machine learning would generate a prediction or classify something like this file is malware.

Imagine a ransomware attack is occurring; the machine learning would detect an anomaly in file access patterns. AI would evaluate the behavior, determine the ransomware, disconnect the infected systems from the network to prevent further damage.”

Aaron Shaha, Chief of Threat Research and Intelligence, at Blackpoint Cyber:

“Machine learning (ML) is a subset of artificial intelligence (AI). When people discuss AI today, they’re often referring to large language models (LLMs), which are essentially ML algorithms trained to recognize patterns in language. These models have shown success because they mimic the way our language—and to some extent, our brains—work.

However, many people conflate AI with the concept of true self-reasoning intelligence. That level of AI, sometimes called artificial general intelligence (AGI), is still many years away. In fact, I’d argue it’s not feasible in the near future, given our limited understanding of human consciousness.”

What future trends do you foresee in the intersection of AI and cybersecurity?

Answers by Víctor Ruiz, CEO and Founder of SILIKN:

“As cybercriminals continue to advance their techniques, AI-driven solutions must evolve to counter these emerging threats. Their ability to adapt and learn from new attack methods will be vital in maintaining the effectiveness of cyber defense strategies.

The intersection of artificial intelligence (AI) and cybersecurity is reshaping digital protection, driving transformative trends across the industry. AI will enable automated incident responses, allowing systems to detect and mitigate threats in real time, while machine learning will enhance predictive threat detection by analyzing extensive datasets and anticipating attack patterns. Moreover, integrating AI with IoT devices will bolster security in interconnected environments, and federated learning will promote global collaboration without compromising data privacy. Supported by AI, the Zero Trust model will ensure stricter access controls, while technologies like blockchain will enhance the integrity and auditability of information. Ultimately, AI-powered solutions will continually adapt to counter increasingly sophisticated cyberattacks, underscoring the importance of proactivity and resilience in future cybersecurity efforts.”

Patricia Thaine, CEO & Founder at Private AI:

“First, the most profound shift will be extending governance to unstructured data. While organizations have built sophisticated security for databases, they remain largely blind to the 80-90% of their information in documents, emails, and messages. AI will finally make this “dark data” visible and manageable, closing massive security gaps.

Second, we’ll see more autonomous security systems that not only detect threats but actively adapt defenses without or with minimal human intervention—a necessity as attacks increasingly operate at machine speed.

Finally, we’ll see security shift from reactive to predictive, with AI systems continuously modeling potential attack paths through digital infrastructure and proactively addressing vulnerabilities before exploitation.

The organizations that thrive will be those that embrace AI not just as a security tool but as a transformative force that fundamentally changes how we conceptualize and implement data protection.”

What advice would you give organizations looking to implement AI in their cybersecurity strategies?

Patricia Thaine, CEO & Founder at Private AI:

“Organizations should take a privacy-first approach when implementing AI in cybersecurity. It’s not just about detecting threats—it’s about ensuring that the AI itself doesn’t become a liability. This means using synthetic data where possible, anonymizing sensitive information, and
continuously monitoring AI models for biases or vulnerabilities. Additionally, companies should invest in training their teams on AI literacy—security professionals need to understand not just how AI can help, but also where it has limitations.”

Conclusion

The rise of AI has upended many industries, yet the term is often misunderstood and concerns about AI are often misplaced. As these opinions indicate, many of its factors will not critically change the way cybersecurity operates, but will rather enable both mass-offense and mass-defense operations and will introduce some new factors into the game. Cybersecurity experts will have to start paying attention to extra concerns and performing additional precautions.

We would again like to express special thanks to the companies/organizations whose leaders took time out of their schedules to share their expertise with Wizcase’s audience:

Private AI – Started by privacy and machine learning experts from the University of Toronto, Private AI’s mission is to build the privacy layer for software. Our team strives for product perfection, while building a culture imbued with generosity, continuous teaching, learning and feedback, and celebrations of each others’ successes.

SEO Locale – At SEO Locale, our mission is to help businesses of all sizes grow with clear, results-driven digital marketing strategies. Based outside of Philadelphia, we focus on providing affordable, high-quality SEO and digital marketing services without long-term contracts. The COO/CTO Nick Quirk has been immersed in the internet space for over 21 years, building a career on innovation and technical expertise. Starting with in-house roles, Nick honed his skills by designing and implementing complex infrastructures and networks, ensuring seamless connectivity and robust systems. His passion for security led him to develop advanced protections for websites and files, effectively preventing attacks and safeguarding critical data. In his current role, he combines his deep technical knowledge with strategic leadership to drive growth and deliver cutting-edge solutions for clients in the ever-evolving digital landscape.
Learn more about SEO Locale here:
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Pluralsight – Pluralsight provides advancements for the world’s tech workforce by unlocking opportunity for the underrepresented, increasing access to tech skill development, and promoting diversity in the tech workforce. Pluralsight has also been ranked no. 10 on Fortune’s Best Workplaces in Technology 2020 list.

ZoogVPN – ZoogVPN is the complete and trusted all-in-one VPN service that protects users’ sensitive personal and financial information online by using a highly encrypted VPN tunnel. Its core mission is “Breaking down Internet barriers and censorship for complete Internet freedom and privacy from anywhere”.

Auraya Systems – Auraya Systems is one of the leading providers of Voice Intellience services, providing AI-powered, biometric voice-based verification and anti-fraud systems for contact centers, web-based applications and numerous other use cases. Auraya’s advanced voice intelligence technology provides next-generation Voice ID, to enable identity and verification for users to get verified in any channel and any language.

Blackpoint Cyber – Blackpoint Cyber is the forerunner in the managed detection and response space, leveraging our proprietary ecosystem to help our partners fight back and win against cyberthreats. We have served the community since 2014 and proudly continue to safeguard businesses around the world.

Spirent – Spirent’s solutions address the rising complexity and expanded security vulnerabilities of next-generation technologies so our clients can deliver the innovative products and services they’ve promised. Spirent focuses on accelerating time to market, reducing complexity, optimizing user experience, and hardening security defenses.

Formstack – For more than 15 years, we’ve helped organizations accelerate work with no-code productivity solutions. Customers all over the world use Formstack to do everything from improving the patient intake process to automating marketing and sales workflows.

Helcim – Helcim believes that by offering small businesses in North America a decidedly human payment processing solution that is easier to sign up and use while being transparent and affordable, we can empower them to grow and prosper.

SILIKN – Cybersecurity is the cornerstone and foundation of all digital services, as it generates the trust in technology that society needs. A graduate of the Socialab México acceleration program, SILIKN is a technology startup that develops and promotes a cybersecurity hub based on open technologies. In this way, SILIKN is creating an innovation ecosystem, through which entrepreneurship and human talent focused on computer security are generated.

WP Security Ninja – For over a decade, Security Ninja has been the guardian of thousands of websites, empowering site owners to navigate the digital space with confidence. This extension has seen a rapid rise in popularity in recent years as more and more tech pros embrace the utility it provides.