To address this challenge, organizations are turning to emergent technologies and forward-thinking strategies. Passwordless authentication, adaptive security models, and invisible AI-driven threat detection are just a few examples of solutions that are rewriting the way businesses secure their systems without compromising user-friendly experiences.

Why security versus usability is a growing challenge

The trade-off between security and usability is today a higher priority concern than ever before. Security needs to meet strict regulatory demands with new AI regulations on top of existing data protection laws, a continued rise in cybercrime including AI-powered attacks, and a low tolerance for breaches on the part of both businesses and their customers. For these reasons, organizations have traditionally favored strong defense, often at the expense of user experience.

But in an increasingly consumer-driven world, frustrating logins, excessive authentication, and chunky security measures can turn users off, leading to revenue loss and eroded trust. Users have little patience for friction, demanding intuitive and near-instant access whether to a banking app, e-commerce site, or corporate platform. Security measures that are too intrusive or cumbersome chase away both customers and employees while inflating support costs.

This dual pressure calls for a paradigm shift: security must be seamless, proactive, and integrated into the user journey rather than an obstructive layer.

3 practical methods for balancing security and usability

The balance between user satisfaction and security is being calibrated in the context of new technological advancements. Implementing sophisticated but user-friendly solutions can improve an organization’s security and enhance usability. Organizations can look to implement passwordless authentication, adaptive authentication and risk-based access, and AI-powered threat detection to help balance cybersecurity with customer experience.

1: Passwordless authentication

Passwords are frequently a weak link in security. Reused, forgotten, or phished credentials open businesses to a huge amount of risk. Password management creates friction for users, from frequent resets to complex requirements.

Passwordless authentication negates this problem entirely. Biometrics—such as fingerprints, facial features, hardware tokens, or single sign-on mechanisms—promise convenient and secure user authentication.

Beyond usability, passwordless systems are, by design, resistant to credential theft, phishing, and brute-force attacks. They also cut IT support costs related to password recovery. But as these systems become more widely deployed, it will be paramount for businesses to make sure that biometric data and token mechanisms uphold trust via secure storage and transmission.

2: Adaptive authentication and risk-based access

Not all users or actions are created equal, and not all are worthy of the same amount of scrutiny. Adaptive authentication dynamically adjusts security measures in real time based on context. For example, a user accessing an account from their usual device and location may only need to take a single login step. If the user logs in from an unfamiliar country or unrecognized device, other verification steps can be called on, such as one-time passcodes or even a biometric check.

Risk-based access further analyzes behavioral patterns, device reputations, and other signals to gauge the chance of malicious activity. With these systems, AI flags anomalies with minimal or no disruption to the legitimized users. Adaptive models minimize friction for the great majority of users while keeping security high.

3: AI-powered threat detection

AI is revolutionizing threat detection and mitigation. Advanced systems monitor a vast amount of data, identify patterns, and predict attacks before they happen. The distinguishing feature of modern security tools driven by AI is that this can all be done invisibly without touching the user experience.

For example, AI can detect credential-stuffing attempts through login pattern analysis or block DDoS (distributed denial-of-service) attacks by identifying spikes in anomalous traffic. These solutions fit nicely behind the scenes in your current infrastructure and provide protection without requiring user input or knowledge.

This invisible layer of AI defense is increasingly helpful for enterprises serving a diverse array of users, from retail customers to corporate employees, all of whom expect security to be a back-end process, not a barrier to use. Third-party AI cybersecurity tools, like Gcore WAAP, are making the adoption of this technology increasingly available and simple.

Practical steps for implementation

The key to the successful integration of these solutions is being strategic in implementing technology so that it aligns with organizational goals. The following steps can get your company started.

• Current system audit: Review current security measures regarding pinpointing specifically where the measures are hurting user experience by accounting for feedback from users in production, along with incident response times.
• Prioritize investments: Your organization must decide which will have the most impact—passwordless authentication or an AI-driven monitoring toolset, for example—and whether they will scale with existing infrastructure.
• Train employees regularly: Employees should be trained on the latest cybersecurity measures implemented within the company. This includes developing an awareness of where new tools are being implemented and how they fit into existing systems. Human error is the top breach vector, so awareness is critical.
• Engage stakeholders: IT and security teams must work closely with business leadership to ensure alignment with organizational priorities.

Balance security and UX with Gcore Edge Security

Balancing security and usability isn’t about compromise; it’s about finding synergy. Advanced tools, such as passwordless authentication, adaptive access control, and AI-driven threat detection, are proving that strong defenses don’t have to come at the expense of user experience. As companies invest in these technologies, they also need to invest in integration and scalability. Security measures should grow with emerging user needs and threats. Only then can success be achieved in the long run.

We offer solutions designed to overcome these challenges. By coupling AI-powered and machine learning technologies with solutions to minimize user inconvenience, Gcore WAAP and DDoS Protection can provide your business with the confidence to secure your systems without disrupting users.

Discover Gcore WAAP, powered by AI

In 2025, the edge cloud landscape will evolve even further, shaping industries from gaming and finance to healthcare and manufacturing. But what are the key trends driving this transformation? In this article, we’ll explore five key trends in edge computing for 2025 and explain how the technology helps with pressing issues in key industries. Read on to discover whether it’s time for your company to adopt edge cloud computing.

#1 Edge computing is integral to modern infrastructure

Edge computing is on the rise and is set to become an indispensable technology across industries. By the end of this year, at least 40% of larger enterprises are expected to have adopted edge computing as part of their IT infrastructure. And this trend shows no signs of slowing. By the end of 2028, worldwide spending for edge computing is anticipated to reach $378 billion. That’s almost a 50% increase from 2024. There’s no question that edge computing is rapidly becoming integral to modern businesses.

#2 Edge computing will power AI-driven, real-time workloads

As real-time digital experiences become the norm, the demand for edge computing is accelerating. From video streaming and immersive XR applications to AI-powered gaming and financial trading, industries are pushing the limits of latency-sensitive workloads. Edge cloud computing provides the necessary infrastructure to process data closer to users, meeting their demands for performance and responsiveness. AI inference will become part of all kinds of applications, and edge computing will deliver faster responses to users than ever before.

New AI-powered features in mobile gaming are driving greater demand for edge computing. While game streaming services haven’t yet gained widespread adoption, the high computational demands of AI inference could change that. Since running a large language model (LLM) efficiently on a smartphone is still impractical, these games require high-performance support from edge infrastructure to deliver a smooth experience.

Multiplayer games require ultra-low latency for a smooth, real-time experience. With edge computing, game providers can deploy servers closer to players, reducing lag and ensuring high-performance gameplay. Because edge computing is decentralized, it also makes it easier to scale gaming platforms as player demand grows.

The same advantage applies to high-frequency trading, where milliseconds can determine profitability. Traders have long benefited from placing servers near financial markets, and edge computing further simplifies deploying infrastructure close to preferred exchanges, optimizing trade execution speeds.

#3 Edge computing will handle big data

Emerging real-time applications generate massive volumes of data. IoT devices, stock exchanges, and GenAI models all produce and rely on vast datasets, requiring efficient processing solutions.

Traditionally, organizations have managed large-scale data ingestion through horizontal scaling in cloud computing. Edge computing is the next logical step, enabling big data workloads to be processed closer to their source. This distributed approach accelerates data processing, delivering faster insights and improved performance even when handling huge quantities of data.

#4 Edge computing will simplify data sovereignty

The concept of data sovereignty states that data is subject to the same laws and regulations as the user who created it. For example, the GDPR in Europe requires organizations to store their citizens’ and residents’ data on servers subject to European laws. This can cause headaches for companies working with a centralized cloud, since they may have to comply with a complex web of fast-changing data sovereignty laws. Put simply: cloud location matters.

With data privacy regulations on the rise, edge computing is emerging as a key technology to simplify compliance. Edge cloud means allows running distributed server networks and geofencing data to servers in specific countries. The result is that companies can scale globally without worrying about compliance, since edge cloud companies like Gcore automate most of the regulatory requirement processes.

#5 Edge computing will improve security

Edge computing is crucial to solving the issues of a globally connected world, but its security story has until now been a double-edged sword. On the one hand, the edge ensures data doesn’t need to travel great distances on public networks, where it can be exposed to malicious attacks. On the other hand, central data centers are much easier to secure than a distributed server network. More servers mean a higher potential for one to be compromised, making it a potentially risky choice for privacy-sensitive workloads in healthcare and finance.

However, cloud providers are starting to add features to their solutions that bring edge security into line with traditional cloud resources. Secure hardware enclaves and encrypted data transmissions deliver end-to-end security, so data will never be accessible in cleartext to an edge location provider or other third parties. If, for any reason, these encryption mechanisms should fail, AI-driven threat scanners can detect and notify quickly.

If your business is looking to adopt edge cloud while prioritizing security, look for a provider that specializes in both. Avoid solutions where security is an afterthought or a bolt-on. Gcore cloud servers integrate seamlessly with Gcore Edge Security solutions, so your servers are protected to the highest levels at the click of a button.

Unlock the next wave of edge computing with Gcore

The trend is clear: Internet-enabled devices are rapidly entering every part of our lives. This raises the bar for performance and security, and edge cloud computing delivers solutions to meet these new requirements. Distributed data processing means GenAI models can scale efficiently, and location-independent deployments enable high-performance real-time workloads from high-frequency trading to XR gaming to IoT.

At Gcore, we provide a global edge cloud platform designed to meet the performance, scalability, and security demands of modern businesses. With over 180 points of presence worldwide, our infrastructure ensures ultra-low latency for AI-powered applications, real-time gaming, big data workloads, and more. Our edge solutions help businesses navigate evolving data sovereignty regulations by enabling localized data processing for global operations. And with built-in security features like DDoS protection, WAAP, and AI-driven threat detection, you leverage the full potential of edge computing without compromising on security.

Ready to learn more about why edge cloud matters? Dive into our blogs on cloud data sovereignty.

Get in touch to discuss your edge cloud 2025 goals

Brute-force attacks are subject to AI enhancements, and AI gives this attack type a huge boost. A brute-force attack is a hacking method that involves systematically trying all possible combinations of passwords or encryption keys until the correct one is found. AI amplifies the threat of brute-force attacks by enabling faster and more efficient guessing through advanced algorithms, making even complex passwords vulnerable if not properly secured.

Read on to discover how these attacks work with AI, why they matter to businesses, and the best ways to defend against AI-powered brute-force threats.

How AI is redefining brute force attacks

Brute-force attacks are based on a simple principle: trial and error. They work by systematically guessing passwords or keys until they match the proper combination. Traditionally, these have been labor-intensive, requiring a substantial amount of time and computational resources to churn through combinations methodically but slowly.

AI completely overturns this model. Trained on huge datasets of password patterns and user behaviors, AI engines bring efficiencies and accuracy never seen before in such attacks. Instead of blind guesses, AI now makes attempts based on probability and patterns. A password that once would have taken weeks to crack can now become compromised in just a matter of hours or minutes.

Another way AI is redefining brute force attacks is by enhancing targeted strategies, such as using leaked username-password pairs to refine guesses. Rather than relying solely on random combinations, AI introduces plausible variations and predicts user tendencies based on patterns in the data. This capability transforms brute force attacks into smarter, more efficient operations, exponentially increasing their success rate and rendering many traditional protections obsolete.

AI performs brute-force attacks with precision

What makes AI really powerful in cyberattacks is its capability for scaling. Linear processes and hardware bottlenecks hamper traditional brute-force efforts; AI overcomes these barriers, mounting simultaneous attacks against diverse systems, often with limited human intervention.

AI-enhanced brute force attacks leverage data to focus their efforts rather than relying on computational force. These attacks are informed by publicly available information scraped from social media, company websites, or breached databases.

For instance, an attacker might use AI to analyze a target’s online presence. Favorite hobbies, pets’ names, or significant dates can all inform password guesses. Even adherence to standard security protocols, like using a mix of characters, offers limited protection against AI’s ability to predict these combinations.

This hyper-personalized approach highlights a troubling reality: Even users who diligently follow traditional best practices can be compromised. AI’s ability to synthesize and exploit contextual data gives it a significant edge.

The business impact of AI brute-force attacks

The unprecedented rate of development and usage in cyberattacks with AI makes traditional cybersecurity tools less effective. For one, static password policies, once a traditional method for securing user accounts, are now obsolete because of computational capabilities and pattern recognition through AI-driven brute force techniques. It can deconstruct the most common patterns people use to create their passwords, predict possible combinations, and do exhaustive attacks-essentially outpacing protections imposed by character complexity or frequent password changes. Predictable human behavior, such as reusing passwords across platforms, worsens the vulnerability by opening up entry points for exploitation.

The consequences of AI-driven brute force attacks can be severe for organizations. A successful attack can grant hackers access to sensitive accounts or systems, leading to data breaches that expose confidential information. This exposure may result in regulatory penalties, such as fines for non-compliance with GDPR or similar laws, and increased costs to remediate security vulnerabilities. The breach can erode customer trust and tarnish an organization’s reputation, potentially causing long-term damage to relationships with clients and partners. For smaller businesses, even one successful brute force attack can disrupt operations to the point of threatening their viability.

How businesses can protect themselves and their customers

The sophistication of brute force attacks compels organizations to adopt advanced and proactive strategies.

• Multi-factor authentication (MFA): MFA adds other verification steps, such as biometrics or temporary codes, making it even more difficult for attackers to succeed even with compromised credentials.
• AI-powered defensive tools: The best way to defeat AI-driven attacks is to employ AI. Advanced monitoring systems can detect unusual patterns in real time and block malicious activities with much greater speed and precision than conventional defenses.
• Passwordless authentication: Giving up passwords altogether will lower the chances of vulnerability. Biometric log-in, hardware security keys, or single sign-on (SSO) systems represent secure and efficient alternatives to passwords.
• Proactive security testing: Regular penetration testing and red-teaming help identify weaknesses before attackers can exploit them. Continuous testing also ensures that defenses keep pace with emerging threats.

Collective action and innovation

Fighting back against AI-driven brute force requires collaboration within and across industries and the use of innovative technology solutions. Businesses must use automated cybersecurity systems with real-time threat detection and adaptable defenses to handle increasingly sophisticated AI threats. AI-driven security platforms like Gcore WAAP can now recognize patterns, block credential stuffing attempts, and mitigate denial-of-service attacks before they escalate.

Cybersecurity providers need to develop scalable and accessible technologies. At the same time, we hope to see governments and regulatory bodies providing ethical AI oversight and regulation to prevent misuse.

Fight fire with fire

AI-powered brute force is a challenge commanding urgent attention from every business. The new threats are smarter, faster, and more relentless than ever, and they demand an immediate shift in cybersecurity strategy. In the line of attack using advanced AI technologies, static defenses with outdated practices will fall short in front of the attackers.

Solutions like Gcore WAAP empower organizations to defend against AI-driven threats with AI-empowered cybersecurity. With its AI-based threat detection and advanced edge security, Gcore WAAP means you won’t be left behind in the ever-evolving threat landscape.

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This article explores the key compliance trends shaping 2025, including data privacy and AI regulations, and outlines actionable strategies to help your business remain compliant while safeguarding your operations against cyber threats.

The tightening grip of data privacy regulations

Data privacy laws have generally become stricter worldwide in recent years. Laws like the EU’s General Data Protection Regulation (GDPR) and California’s Consumer Privacy Act (CCPA) have set a high bar regarding privacy standards, and new regulations in countries like India, Brazil, and even China add layers of complexity. The trend in 2025 seems quite clear: governments are enacting more comprehensive laws that demand increased accountability and transparency from businesses.

For example, regulations now focus on user consent, secure data storage, and stricter breach notification timelines. Companies will also have to consider the regional nuances in the way laws are applied. Cross-border data transfers, especially between jurisdictions with differing standards, have come under increasing scrutiny.

Specifically, regulations such as PCI and HIPAA are cracking down on consumer privacy laws. By March 25, 2025, PCI will require the implementation of advanced defense solutions such as WAF for compliance, while HIPAA enforces harsh penalties on non-compliance that can reach up to $1.5 million a year. Non-compliance with other regulatory bodies can also result in large fines. For example, CCPA fines generally range between $2500 to $7500 per single violation, and GDPR fines can reach a staggering €20 million, or 4% of the organization’s total global turnover.

Businesses need robust mechanisms that can help them comply with diverse laws and avoid the consequences of non-compliance while maintaining seamless data operations across borders. Another option is to outsource compliance by using a global technology infrastructure provider like Gcore, automating adherence to local storage laws.

AI governance enters the spotlight

The integration of AI into daily business processes has led to the development of AI governance frameworks. These frameworks handle ethical concerns, reduce algorithmic biases, and increase transparency. For companies, this means following a set of guidelines that dictates how AI processes sensitive data and interacts with users.

In 2025, organizations that have been using AI-powered tools for analytics, customer service, or threat detection must be ready for audits that scrutinize AI-driven decision-making processes. Compliance will involve documenting AI workflows, assessing the fairness of algorithms, and avoiding the misuse of AI technologies in ways that might infringe on individual privacy rights.

AI governance is far more than just a regulatory requirement; it’s a trust-building measure. As customers grow wary of how their data is used, demonstrating ethical AI practices can enhance customer confidence and loyalty.

Navigating cross-border data transfer laws

Globalization has integrated the economy digitally, but there are still some challenges in managing data transfer between regions with different compliance standards. Regulations such as GDPR do not allow data transfers to countries with relatively weak data protection laws, compelling businesses to create additional safeguards.

Geopolitical dynamics will complicate these challenges in 2025. An increasing number of countries are developing data residency laws and other localized data storage mandates that require data to stay within their borders. Businesses must start investing in region-specific infrastructure or finding service providers that can meet these local mandate requirements.

The role of security in compliance

Security and compliance are interrelated. The threat landscape changes daily, and organizations must prove their rigorous security standards to counter emerging threats in order to meet regulatory expectations. The development of ransomware, phishing campaigns, and AI threats places greater burdens on organizations to safeguard their systems.

Modern no-touch security solutions are at the heart of compliance today, from encryption, which protects sensitive data, to intrusion detection systems that flag unauthorized access attempts. Such solutions help organizations take legal standards into account when planning their self-defense efforts. This can be further enhanced through real-time monitoring and mechanisms for automated response to better cope with dynamic threat landscapes.

Why non-compliance is not an option

By 2025, the implications of non-compliance will extend beyond sanctions and fines. Data breaches and violations result in damaged reputations, disruptions of customer trust, and interference with business processes. To survive in competitive markets, organizations will need competitive differentiators such as compliance. Compliance shows that an organization is ethical and serious about its customers’ security, which will benefit customers, investors, and other stakeholders.

5 proactive strategies for staying compliant

While changing regulations may make compliance feel more arbitrary than ever and tough to understand, proactive strategies can help organizations stay ahead of fast-evolving regulations.

1. Continuous monitoring and auditing

The complexity of modern compliance requires constant monitoring. Companies should establish tools that can facilitate real-time visibility into the flow of data, permissions for access, and the realization of vulnerabilities. Regular audits help ensure that all systems and processes are within the confines of regulatory standards and can withstand investigations into possible infractions.

2. Adaptive security technologies

Compliance meets the legal requirements demanded by regulating bodies and creates a secure environment that prevents breaches and unauthorized access. Advanced security, such as risk-based access control and behavioral monitoring, significantly improves protection and compliance. These technologies adapt to emerging threats while automatically enforcing security policies across systems.

3. Automation

Automation has become key to maintaining compliance. By automating most routine tasks, such as record-keeping, reporting, and monitoring access, compliance processes can be made less prone to error and simpler. Automation also means an organization can easily scale its security and compliance as it expands.

4. Employee training and awareness

Human error remains a major cause of data breaches. Regular training ensures that employees are compliant when it comes to protecting sensitive information and able to recognize phishing attempts. Compliance training needs to be a continuous process, with updates as laws and standards evolve. For example, AI phishing presents a new challenge to businesses, likely requiring employee re-training.

5. Trusted service providers

Vendors or service providers that prioritize global compliance can significantly reduce a business’ workload. Choosing a platform with already-developed compliance features and edge capabilities—like Gcore—means your organization is one step ahead in preparing for regulatory challenges. This can reduce the human resources required to comply, automating most compliance processes across regions.

How Gcore simplifies global compliance

Companies facing compliance challenges need trustworthy, scalable solutions to address security and regulatory demands simultaneously. To that end, Gcore developed a variety of advanced security solutions.

• Gcore WAAP protects organizations from the most relevant threats while securing data integrity
• Gcore DDoS Protection reduces the risk that could lead an organization to non-compliance with incident response timelines
• Gcore CDN enables seamless data transfers, conforming to cross-border requirements thanks to a global network of 180+ points of presence

By combining some of the world’s most progressive security technologies with a commitment to user experience, Gcore enables organizations to reduce compliance complexity while staying one step ahead of emerging threats. With the right tools and a proactive approach, businesses can turn compliance from a challenge into an opportunity for growth and innovation.

Get a complimentary consultation about your business’ global compliance requirements

Cybercriminals are using AI to automate and scale operations, enabling quicker, stealthier, and adaptive attacks. To counter this threat, cybersecurity professionals employ AI-driven systems that, with predictive analytics, real-time detection, and automated responses, try to stay one step ahead of their adversary in the constantly shifting game of technological cat and mouse. Read on to discover how AI helps hackers create adaptive, stealthy threats and how cybersecurity teams leverage AI to counter them.

The evolution of adversarial AI in cybercrime

AI has been a strong enabler of cybercrime. By automating complex tasks, generating convincingly human-like content, and analyzing vast datasets, AI amplifies the scale and efficiency of attacks. Cybercriminals increasingly leverage AI to enhance the sophistication and effectiveness of their attacks.

Notable instances from the past year include:

• AI-generated disinformation campaigns: The US Treasury Department sanctioned Russia’s Center for Geopolitical Expertise for using AI tools to create and disseminate disinformation during the 2024 presidential election. This organization managed a network of at least 100 websites and employed AI to generate manipulated media targeting US political figures.
• AI in cyber warfare: Government officials highlighted concerns over adversaries harnessing AI to conduct cyberattacks against national infrastructure. For instance, Russia was reported to be using AI to launch sophisticated cyber operations aimed at disrupting critical services and deterring support for Ukraine.
• AI-enhanced phishing attacks: A staggering 96% of organizations reported being impacted by AI-powered phishing attacks in 2024. These attacks utilized AI to craft highly convincing messages, making it challenging for individuals and organizations to distinguish between legitimate and malicious communications.

AI-driven cybersecurity: building the defense

Cybersecurity teams are reacting to these dangers by integrating AI into their strategy, making good use of its massive-scale data processing, anomaly detection capability, and autonomous responses to combat newly emerging threats. Several machine learning models scan through network traffic, user behavior, and attack patterns for known breaches in history. Correlating this data allows AI-powered tools to flag even incredibly subtle deviations from normal activities.

For example, AI-powered behavioral analytics track user activities across systems to identify and block access attempts that are unauthorized and might become incidents. Automation is also critical: AI-powered incident response platforms can initiate predefined protocols and isolate infected systems, notify stakeholders, and start remediation within seconds of attack detection. Automated responses reduce human error and minimize the window attackers have for exploitation.

Simulating tomorrow’s cyber wars

Artificial intelligence has redefined how cybersecurity threats are identified and mitigated. A major trend in this digital arms race is using AI to simulate attacks with the goal of strengthening defenses. Security teams now deploy adversarial AI to mimic potential threats, pinpointing weak spots in systems and proactively hardening them before real attackers strike. This approach is known as red-teaming.

At the same time, hackers are moving forward by building AI capabilities into automated reconnaissance. Such systems can scan large networks, identify potential vulnerabilities, and tailor attack vectors in near real-time.

This results in a highly dynamic battlefield, with continuous developments from both sides leveraging the power of AI for innovating at a faster speed. The introduction of AI-as-a-service platforms has shifted this balance as prebuilt malicious AI tools are now available even to lower-skilled attackers. In this way, the ability to conduct complex cyberattacks is becoming democratized in a way that demands agile and sophisticated defenses. To meet this challenge, out-of-the-box AI-powered cybersecurity solutions have entered the market, such as Gcore WAAP. These give businesses the power to match fire with fire—even if they lack in-house AI expertise.

The interplay between human and artificial intelligence

While AI speeds up attacks and defenses alike, human input remains at the core of cybersecurity. Criminals and cybersecurity professionals alike know AI’s strengths and weaknesses and human ingenuity’s advantages.

Attackers experiment with ways of using adversarial machine learning to break through all weak points in the AI systems’ defenses. Along these lines, the attackers will make subtle modifications to the input data with a view of fooling the algorithms to misclassify malicious activity as benign. In that light, researchers have shown just how small changes in images or files will allow these files to pass through malware detection engines driven by artificial intelligence.

On the defense side, security analysts develop AI models that adapt dynamically to evolving threats. AI systems often require human oversight to keep them accurate and unbiased, as attackers will usually try to exploit weaknesses in the data sets used to train them. For example, AI-powered spam filters can become inefficient if attackers flood them with new phishing templates made to avoid existing rules.

Get AI-driven security for the AI-driven era

Businesses can only counter the growth of such threats with progressive security frameworks that combine state-of-the-art AI with human judgment. Continuous monitoring of digital environments, such as communications systems, third-party systems, or web apps, is now vital for threat detection and fixation as they emerge. In addition to surveillance, red-teaming has become an essential practice in modern cybersecurity strategies, with adversarial AI conducting exercises to test system robustness.

Investing in advanced tools such as Gcore WAAP helps organizations protect their edge against a rapidly changing threat landscape by providing AI-powered protection. Cybercriminals continue to improve their arsenal, and organizations must ensure that they’re investing equally in their defenses.

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President Trump immediately declared DeepSeek a wake-up call for the US, while Meta was said to be “scrambling war rooms of engineers” seeking ways to compete with DeepSeek in terms of low costs and computing power. But if the normally bullish American government and tech giants are rattled by DeepSeek, where does that leave the more highly regulated and divided Europe in terms of keeping up with these AI titans?

Multiple sources have already expressed concerns about Europe’s role in the AI age, including the CEO of German software developer SAP, who blamed the silos that come with individual countries having different domestic priorities. European venture capitalists had a more mixed view, with some lamenting the slower speed of European innovation but some also citing DeepSeek’s seeming cost-effectiveness as an inspiration for more low-cost AI development across the continent.

With an apparent AI arms race developing between the US and China, is Europe really being left behind, or is that a misperception? Does it matter? And how should the continent respond to these global leaps in AI advancement?

Why does it seem like the US and China are outpacing Europe?

China and the US are racing ahead in AI due to massive investments in research, talent, and infrastructure. China’s government plays a significant role by backing AI as a national priority, with strategic plans, large data sets (due to its population size), and a more flexible regulatory environment than Europe.

Similarly, the US benefits from its robust tech industry with major players like Google, OpenAI, Meta, and Microsoft, as well as a long-standing culture of innovation and risk-taking in the private sector. The US is also the home of some of the world’s leading academic institutions, which are driving AI breakthroughs. Europe, by contrast, lacks some of these major drivers, and the hurdles that AI innovators face in Europe include the following:

Fragmented markets and regulation

Unlike China and the US, Europe is made up of individual countries, each with their own regulatory frameworks. This can create delays and complexities for scaling AI initiatives. While Europe is leading the way on data privacy with laws like GDPR, these regulations can also slow innovation. Forward-thinking EU initiatives such as the AI Act and Horizon Europe are also in progress, albeit in the early stages.

Compare this to China and the US, where regulations are minimalist with the goal of driving innovation. For instance, collecting large datasets, essential for training AI models, is much easier in the US and China due to looser privacy concerns. This creates an innovation lag, especially in consumer-facing AI.

The US used to have national-level regulation, but that was revoked in January 2025 with Trump’s Executive Order, and some states have little to no regulation, leaving businesses free to innovate without barriers. China has relatively strict AI laws, but they’re all applied consistently across the vast country, making their application simple compared to Europe’s piecemeal approach. All of this has the potential to incentivize AI innovators to set up shop outside of Europe for the sake of speed and simplicity—although plenty remain in Europe!

Talent drain

The US and China can attract the best AI talent due to financial incentives, fewer regulatory barriers, and more concentrated hubs (Silicon Valley, Beijing). While many AI experts trained in Europe, they often move abroad or work with multinational corporations that are based elsewhere. Europe has excellent academic institutions, but the private sector can struggle to keep talent within the region.

Funding gaps

Startups in Europe face more challenges in terms of funding and scaling compared to those in the US or China. Venture capital is more abundant and aggressive in the US, and the Chinese government heavily invests in AI companies with a clear, state-backed direction. In contrast, European investors are often more risk-averse, and many AI startups struggle to get the same level of backing.

How should Europe respond to global AI innovations?

While Europe may not be able to compete with the wealth, unification, and autonomy of either China or the US, there are plenty of important areas in which it excels, even leading these other players. Besides that, caution and stricter adherence to ethical regulations may be beneficial in the long run. Last year, the previous US administration commissioned a report warning of the dangers of AI evolving too quickly. Europe’s more “slow and steady” approach is more likely to mitigate these risks.

At the same time, Europe should aim to foster innovation as well as take advantage of AI developments in other markets. Here are some more ways in which European companies can take advantage of their regional positioning to get ahead in the global AI market:

• Innovation in niche areas: Europe may not be able to lead in general-purpose AI like the US or China, but it can carve out spaces in areas like ethical AI, AI governance, and privacy-focused AI. European companies could also specialize in areas like AI for healthcare, environmental sustainability, or manufacturing, where the continent has existing strengths.
• Collaboration over competition: European nations might need to focus on collaborative efforts. By pooling resources, sharing expertise, and aligning on common goals, Europe can build a unified approach to AI that is both innovative and cohesive. This collaborative model could help Europe create AI frameworks that are sustainable, inclusive, and ethically responsible, all while fostering a spirit of teamwork rather than rivalry.
• AI sovereignty: AI sovereignty means aiming to ensure that Europe isn’t overly dependent on American or Chinese tech giants and keeps European data in Europe. This involves building localized infrastructure, developing homegrown AI solutions, and protecting European data—while ensuring European AI remains competitive globally. European sovereignty and the region’s tight regulations are likely to catch the eye of the international AI market in light of the already-emerging concerns regarding DeepSeek and censorship, which may be offputting for markets outside of China.

So, while the US and China are making the headlines right now, Europe is more quietly paving its own areas of AI specialization, characterized by concern for data privacy and ethics. We’re curious to see whether the global AI market will turn its attention to the benefits Europe offers during 2025. Whether or not European AI companies become top news stories, there’s no doubt that we’re already seeing incredible quality AI models coming out of the continent, and exciting projects in the works that build on key industries and expertise in the region.

Talk to us about your AI needs

No matter where in the world your business operates, it’s essential to keep up with changes in the fast-paced AI world. These constant shifts in the market and rapid innovation cycles can create both opportunities and challenges for businesses. While it may be tempting to jump on the latest bandwagon, businesses should carefully examine the pros and cons for their specific use case, and keep in mind their regulatory responsibilities.

Whether you’re operating in Europe or globally, our innovative solutions can help you navigate the fast-moving world of AI. Get in touch to learn more about how Gcore Everywhere Inference can support your AI innovation journey.

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For many companies leveraging AI, DeepSeek’s rise signals a shift that demands attention and strategic evaluation. In this article, we’ll explore DeepSeek’s emergence, unique value proposition, and implications for businesses across industries.

Why is DeepSeek disrupting the AI industry?

DeepSeek’s approach represents a fundamental shift in AI development. While most popular AI models rely on expensive and complex NVIDIA chips, DeepSeek trained its DeepSeek-R1 model using fewer, less sophisticated ones, delivering comparable performance at a fraction of the cost.

Here’s what sets DeepSeek apart:

1. Open-source accessibility: DeepSeek-R1’s open-source nature is a game-changer, especially for startups and small-to-medium enterprises (SMEs). By offering a flexible and customizable model, DeepSeek lowers the barriers to entry for businesses exploring AI adoption.
2. Performance and scalability: DeepSeek-R1 rivals the capabilities of some of the most advanced LLMs on the market. Its scalability ensures businesses can deploy it across various applications, from customer service chatbots to dynamic content creation, without sacrificing performance.
3. Strategic localization: Its focus on the Chinese market and its growing global footprint cater to localized needs that other AI providers often overlook. This regional focus enhances its appeal to businesses operating in diverse markets.

Implications for businesses

DeepSeek’s rise is more than just a story of technological innovation—it’s changing how businesses use AI. Focusing on open-source solutions and adapting to local needs sets new expectations and encourages companies to think differently about integrating AI. This shift brings both exciting opportunities and challenges for businesses in various industries.

Opportunities

• Cost-effective AI integration: DeepSeek-R1’s open-source nature can significantly reduce barriers to entry for businesses exploring AI adoption. This democratization of AI levels the playing field and fosters innovation.
• Enhanced customization: While many LLMs allow customization, DeepSeek-R1’s open-source framework offers unparalleled flexibility. Businesses can fully modify the model to meet specific needs, such as industry-specific language processing or compliance requirements. This level of adaptability, combined with DeepSeek’s affordability, makes it a standout choice for startups and SMEs seeking cost-effective, tailored AI solutions.
• Competitive edge: Early adopters of DeepSeek’s technology can gain a strategic advantage by leveraging its advanced features, such as real-time natural language processing (NLP) for dynamic customer interactions, improved multilingual capabilities for global reach, and adaptive learning algorithms that fine-tune outputs based on user-specific data. These tools improve efficiency, enhance customer experiences, and streamline operational scalability.

Challenges

• Navigating a fragmented market: DeepSeek’s rise adds complexity to an already competitive AI landscape. Businesses must carefully evaluate their options to make sure they choose the best solution for their needs.
• Data privacy considerations: While open-source models offer transparency, they also require businesses to be aware of data security and compliance requirements, especially in regions with stringent regulations.
• Integration efforts: Adopting a new AI model often requires reconfiguring existing workflows and infrastructure, which can be resource-intensive.

How will the AI industry respond?

DeepSeek’s entry into the marketplace has sparked a critical question: how will established players in the AI industry adapt to this new competitor? Traditional industry leaders like OpenAI, Microsoft, and Google will likely accelerate their own innovations to maintain dominance. This could mean faster deployment of advanced AI models, increased investments in proprietary technologies, or even adopting open-source strategies to stay competitive.

Meanwhile, the growing popularity of open-source models might pressure bigger players to lower costs or improve accessibility for businesses. Collaboration with startups and regional AI developers could also become a focus as companies aim to diversify their offerings and tap into localized markets that DeepSeek is currently dominating.

For businesses relying on AI, the break-neck speed of change means that staying agile and exploring new opportunities is non-negotiable. The emphasis on transparency, affordability, and regional adaptation may redefine what companies look for in AI solutions, making it an exciting time for innovation and growth in the industry.

Driving innovative AI solutions

DeepSeek’s impact reminds us that the AI industry remains dynamic and unpredictable. By leveraging innovative solutions like DeepSeek-R1, businesses can unlock new possibilities and thrive in an increasingly AI-driven world.

Companies need trustworthy partners to navigate this dynamic environment as the AI landscape evolves. Gcore provides innovative AI solutions that enable businesses to stay competitive and evolve. With our platform’s scalable AI infrastructure and seamless deployment options, you can effectively and efficiently harness the power of AI.

Unlock the full potential of DeepSeek’s capabilities. Deploy it seamlessly with Gcore’s Everywhere Inference for scalable, low-latency AI.

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From static threats to self-evolving AI

Traditional threats follow predefined logic. For example, malware encrypts data; phishing schemes deploy uniform, poorly disguised messages; and brute-force attacks hammer away at passwords until one works. Static defenses, such as antivirus programs and firewalls, were designed to address these challenges.

The landscape has shifted with AI’s ubiquity. While AI drives efficiency, innovation, and problem-solving in complex systems, it has also attained a troubling role in cybercrime. Malicious actors use it as a tool to create threats that become smarter with every interaction.

Self-evolving AI has emerged as a dangerous development: an intelligence that continuously refines its methods during deployment, bypassing static defenses with alarming precision. It constantly analyzes, shifts, and recalibrates. Each failed attempt feeds its algorithms, enabling new, unexpected vectors of attack.

How self-learning AI threats work

A self-evolving AI attack functions by combining machine learning capabilities with automation to create a threat that uses constantly adapting strategies. Here’s a step-by-step of the process:

1. Pre-attack surveillance: Before making an infiltration, AI conducts reconnaissance, gathering intelligence on system configurations, vulnerabilities, and active defenses. But what sets self-evolving AI apart is its ability to process immense amounts of information with unprecedented speed, covering an entire organization’s digital footprint in a fraction of the time it would take human attackers.
2. Initial penetration: Entry methods can include exploiting outdated software, using weak credentials, or leveraging convincing social engineering tactics. The AI automatically selects the best breach strategy and often launches simultaneous probes to find the weak links.
3. Behavioral modifications: When detected, AI behavior changes. A flagged action causes immediate recalibration: encrypted communication pathways, subtle mimicry of benign processes, or the search for alternative vulnerabilities. Static defenses become ineffective against this continuous evolution.
4. Evasion and anti-detection techniques: Self-learning AI employs advanced methods to evade detection systems. This includes generating synthetic traffic to mask its activities, embedding malicious code into legitimate processes, and dynamically altering its signature to avoid triggering static detection rules. By mimicking normal user behavior and rapidly adapting to new countermeasures, the AI can stay under the radar for extended periods.
5. Post-infiltration activity: Even once the AI has access to the data or has achieved system compromise, it continues to adapt. As the system’s defenses rise to meet the challenge, so does the attack, using decoys, strategic retreat, or further adaptation to avoid detection.

The result? Threats that seem to have a life of their own, responding dynamically in ways that stretch traditional security measures past their breaking point.

How adaptive AI threats impact businesses

One example of how self-evolving AI cyberattacks harm businesses is phishing—a traditional cyberattack mechanism that has taken on a new guise. With AI, spear-phishing campaigns have gone from crude, scattershot operations reliant on guesswork to weapons of precision. Data mined from email exchanges, social media profiles, and behavioral patterns helps the attacker craft messages indistinguishable from real correspondence. Every interaction further tunes the AI in its quest to manipulate its targets, fooling even the most skeptical recipients.

AI-powered malware outperforms traditional malware by leveraging real-time adaptability and intelligence, particularly in large-scale infiltrations like corporate network breaches. For example, instead of relying on a single method of attack, it can actively monitor live network traffic to detect vulnerabilities, identify valuable assets such as sensitive data or critical infrastructure, and dynamically adjust its tactics based on the environment it encounters. This might include switching between different penetration techniques, such as exploiting unpatched software vulnerabilities, mimicking legitimate network activity to avoid detection, or deploying customized payloads tailored to specific systems. This level of situational awareness and adaptability makes AI-driven malware attacks far more stealthy, precise, and capable of causing significant harm.

Ransomware is a type of malicious software designed to block access to a system or encrypt critical data, holding it hostage until a ransom is paid. Traditional ransomware often uses brute-force tactics, encrypting files across an entire system indiscriminately. Victims are typically presented with a demand for payment, usually in cryptocurrency, to regain access. What makes ransomware particularly devastating is its ability to cripple operations, disrupt critical services, and exploit vulnerabilities in organizations unprepared for such attacks.

Healthcare systems are especially attractive to ransomware attackers for several reasons. Hospitals and clinics rely heavily on interconnected devices and digital systems to provide care, from managing patient records and diagnostic tools to operating life-saving equipment. This dependency creates an environment where even a brief disruption can have life-or-death consequences, making healthcare organizations more likely to pay ransoms quickly to restore functionality. In addition, the highly sensitive nature of patient data—medical histories, insurance details, and personal identifiers—makes it incredibly valuable on the black market, further incentivizing attackers. Self-evolving ransomware compounds these risks by using AI to identify high-value targets within a network, tailor its attacks to specific vulnerabilities, and avoid detection, making it a particularly dangerous threat to an already vulnerable sector.

Why static defenses fail and the case for adaptive, AI-powered defenses

The root problem static defenses face is predictability. Traditional security measures, such as antivirus tools and intrusion detection systems, operate on a pattern recognition model. They look for known attack signatures or deviations from established norms. Self-evolving AI doesn’t follow these rules, bypassing pattern recognition defenses by being unpredictable and changing itself faster than static measures can keep up with.

Even polymorphic malware, which changes identifying markers in an attempt to evade detection, falls short. While polymorphic threats rely on pre-coded variability, AI-driven attacks learn and respond to changes in their environment. What worked to block one version of the attack may fail spectacularly against version two, deployed mere seconds later.

The counter to self-evolving AI-powered threats has to be equally intelligent. Static tools must be replaced by adaptive solutions that monitor, learn, and respond on the fly against evolving attacks.

Some key components of an adaptive solution include:

• Behavioral monitoring: Advanced tools that analyze activity patterns to detect anomalies rather than rely on static rules. For example, unusual login times or data access behavior trigger real-time alerts, even without pattern deviations.
• Dynamic threat neutralization: AI-powered web application and API protection (WAAP) solutions are a particular standout in dynamic threat neutralization. These systems adjust defenses on the fly, applying machine learning models to identify and block adaptive threats without manual intervention.
• Proactive identification: Instead of waiting for attacks, modern tools actively search for vulnerabilities and suspicious activities, reducing the likelihood of successful infiltration.
• Automation and coordination: AI-based security systems integrate seamlessly across the organization’s ecosystem. Once a threat is detected, the response propagates itself network-wide, automatically executing containment and mitigation.

Learn more about why AI-powered cybersecurity is the best defense against AI threats in our dedicated blog.

Augmenting human expertise with adaptive tools

Security professionals remain indispensable. Adaptive tools don’t replace human expertise; they enhance it. With AI-powered solutions, DevSecOps engineers can decipher intricate attack patterns, anticipate the next move, and craft strategies that stay ahead of even the most sophisticated threats.

For leadership, the message is clear: investment in advanced security infrastructure is no longer a challenge that can be pushed aside to be dealt with in the future, but an immediate requirement. The longer one delays action, the more vulnerable the systems are to threats that are becoming more effective, harder to detect, and increasingly challenging to mitigate.

Combat AI-driven cyberthreats with Gcore

The self-evolving nature of AI-driven cyber threats forces organizations to completely reevaluate their security strategies. Advanced threats change the landscape of adaptability, bypass conventional defenses, and challenge teams to reconsider their strategies. Still, with increasingly sophisticated cyberattacks, adaptive countermeasures powered by AI have the potential to become equally complex and rebalance the equation.

For organizations eager to embrace dynamic defense, solutions such as Gcore WAAP have become a much-needed lifeline. Driven by AI, Gcore WAAP’s adaptability means that defenses will keep evolving with threats. As attackers change their tactics dynamically, WAAP changes its protection mechanisms, staying one step ahead of even the most sophisticated adversaries.

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This highly anticipated move has already sparked debate about what it means for the future of AI in the US. For some, it represents an opportunity for rapid innovation. For others, it raises concerns about the ethical implications of deregulation.

Alongside the repeal, Trump also announced a $500 billion AI investment initiative, named Stargate, which aims to accelerate advancements in AI and related technologies. Together, these actions highlight the administration’s focus on positioning the US as an AI leader.

In this article, we’ll explain what Biden’s executive order set out to achieve, why Trump scrapped it, and the implications for businesses both in the US and around the world. We’ll also explain how your company can stay ahead of AI regulatory changes.

What was Biden’s AI Safety Executive Order about?

Introduced in 2023, Biden’s AI Safety Executive Order aimed to set comprehensive safeguards for the development and deployment of AI technologies. Key provisions included the following:

• Mandatory safety testing: AI developers were required to submit safety test results to federal authorities before releasing products with potential societal impacts, such as tools used in national security, healthcare, or finance.
• Standardized testing frameworks: The National Institute of Standards and Technology (NIST) was tasked with creating consistent safety-testing protocols for AI systems.
• Risk mitigation: Federal agencies were instructed to evaluate AI-related risks, including cybersecurity vulnerabilities, algorithmic bias, and potential misuse.

The Executive Order reflected a growing need to address the risks of rapidly advancing and proliferating AI technologies. It aimed to improve transparency and accountability, but at the risk of slowing down development.

Why did Trump scrap the AI Safety Executive Order?

The Trump administration framed the repeal as a necessary measure to remove bureaucratic obstacles to innovation. Officials argued that Biden’s regulations stifled creativity, particularly for smaller companies, and created delays in AI product development that risked the US’s status as an AI powerhouse.

Trump’s approach aims to encourage a more competitive environment where businesses can experiment with AI freely without federal oversight. This aligns with his broader vision of reducing regulation across industries.

However, critics warn that deregulation may exacerbate issues like AI bias, unethical applications, and cybersecurity risks. Trump’s administration, for its part, maintains that the market itself will incentivize responsible practices, as businesses strive to build trust with their customers.

What does this mean for businesses?

Despite this repeal of the regulation being US-specific, this policy change will have global effects. The likely impact depends on whether or not your business operates in the US.

For US companies

The removal of federal guidelines creates a more flexible but potentially volatile operating environment.

1. Faster innovation cycles: Startups and small-to-midsize enterprises, previously hampered by compliance costs, may now find it easier to enter the market and innovate rapidly.
2. Increased market competition: Relatedly, deregulation could foster a more competitive landscape, as companies race to capitalize on the freer environment.
3. Self-regulation becomes crucial: With federal oversight diminished, companies must take responsibility for ethical AI practices. Those failing to self-regulate risk damaging their reputations, especially as consumers and partners demand transparency.

It’s worth noting that this repeal does not affect state-specific regulations, so your company may still have legal obligations to comply with AI-related laws in states like California. You can learn more about these regulations in our blog article about AI regulations in North America.

For non-US businesses

The repeal’s effects extend beyond the US border because AI is a globally connected industry. Even if your business doesn’t operate in the US, it’s worth being aware of the policy shift’s knock-on effects.

1. Competitive pressure from US companies: With fewer regulatory constraints, US firms may innovate more aggressively, creating challenges for non-US competitors operating in stricter regulatory environments such as companies based in the EU operating under the AI Act.
2. Navigating regulatory divergence: International businesses working with US partners or customers may face a patchwork of ethical and compliance expectations, even if your business isn’t based in the US. Working with a global AI provider like Gcore can make it easier to keep up with regulatory changes, and you can pass on that benefit and compliance to your customers.

Adapt to AI policy shifts with agility

AI businesses must remain agile and adaptable in light of these changes. Regulatory landscapes can shift overnight, creating opportunities and challenges. The key to success lies in balancing innovation with ethical responsibility. The removal of regulatory guardrails may spur rapid innovation, particularly among smaller players. However, it also places greater responsibility on companies to self-regulate and maintain trust.

Whether you’re deploying AI in the US or looking to simplify your global AI operations, Gcore’s end-to-end AI solutions can help. Get in touch for a personalized consultation and discover how Gcore Edge AI can support your AI innovation journey.

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In this article, we discuss the regulatory landscape in US and Canada, examining how companies can innovate in AI while remaining compliant with the letter of the law. Stay tuned for future articles looking at different regions.

The US: A fragmented approach to AI regulation

The United States is gradually building a regulatory structure around AI, but it’s still fragmented: Efforts are taking place both at the federal and state levels, with state governments driving many AI-related laws. This patchwork of rules poses challenges for businesses operating across state lines, as they must navigate varying compliance requirements.

The repeal of the AI Bill of Rights

On January 21, 2025, the Biden administration announced the repeal of the Blueprint for an AI Bill of Rights. Originally introduced in 2022, this document outlined ethical guidelines for AI usage and set the stage for future regulation. While not legally binding, the blueprint had emphasized principles such as safe and effective systems, protections against algorithmic discrimination, data privacy, transparency, and human oversight.

The repeal reflects a shift in regulatory priorities and has raised questions about the future of AI governance in the US. Critics argue that removing the blueprint leaves a gap in ethical guidance for AI development and deployment, while proponents claim it lacked enforceability and failed to address the fast-evolving AI landscape.

Despite its repeal, the AI Bill of Rights still influences ongoing state-level legislation and industry best practices. Businesses should remain aware of these principles, as they are likely to inform future regulatory efforts.

Key principles: what businesses can retain from the AI Bill of Rights

Although the blueprint is no longer in effect, its foundational ideas continue to resonate as they were in effect during a formative period for AI. Businesses can still use these principles to align their AI strategies with emerging ethical standards:

• Safe and effective systems: Businesses should continue to prioritize safety and reliability in AI. Testing systems rigorously, involving diverse stakeholders in development, and conducting independent audits remain essential for mitigating risks. This is particularly critical in sensitive industries like healthcare and finance.
• Algorithmic discrimination protections: Bias in AI systems is a pressing issue. The repeal doesn’t negate existing regulatory scrutiny, such as the Equal Employment Opportunity Commission’s (EEOC) initiative on AI hiring practices. Companies must proactively monitor and address bias to avoid reputational and legal risks.
• Data privacy: With the repeal, states like California will likely take a more prominent role in shaping data privacy standards.
• Transparency: Transparency remains vital for building trust. Even without federal guidance, industries using AI should aim to provide clear explanations of AI decisions, particularly in high-stakes areas like healthcare and financial services.

Human oversight: The principle of maintaining human alternatives to AI decisions is widely regarded as a best practice. Businesses should continue to implement mechanisms for human review and appeals to maintain consumer confidence and regulatory alignment.

State-level regulations: California leading the charge

While federal guidelines shape AI governance on a large scale, specific states have rapidly scaled up their version of legislation, greatly influencing how AI is implemented. Leading the charge is California.

Since 2018, California has been enforcing the California Consumer Privacy Act (CCPA). This law greatly amplifies consumer privacy protections while imposing rigid rules of data handling on businesses. The fines for failure to follow these rules can rise to $7,500 for each intentional violation, making compliance essential for any business operating within or even just serving California’s market. These penalties are more than just a slap on the wrist. In addition to fines, companies can face serious reputational and financial consequences for non-compliance.

The CCPA doesn’t just offer vague promises to protect personal data. It lays down concrete rights for California residents. They can ask companies exactly what personal information they’ve collected, how it’s used, and even request its deletion. That’s a big deal. And if someone doesn’t want their data sold or shared? They have the right to opt out. Businesses, in turn, can’t refuse these requests or discriminate against anyone exercising their rights. This goes beyond surface-level protections—people can request that their data be corrected if it’s wrong and limit how sensitive data like financial info or precise geolocation is used. These rights aren’t limited to just big companies either; if a business collects data from California residents, it’s bound by the CCPA’s rules.

Beyond California

But California’s not alone. Seventeen states have passed a combined total of 29 bills regulating AI systems, mostly focused on data privacy and accountability. For instance, Virginia and Colorado have rolled out the Virginia Consumer Data Protection Act (VCDPA) and the Colorado Privacy Act (CPA), respectively. These efforts reflect a growing trend of state-level governance filling in the gaps left by slow-moving federal legislation.

States such as Texas and Vermont have even set up advisory councils or task forces to study the impact of AI and propose further regulations. By enacting these laws, states aim to ensure that AI systems not only protect data privacy but also promote fairness and prevent algorithmic discrimination.

These state initiatives, while beneficial to AI regulation, create a complex web of regulations that businesses must keep up with, especially those operating across state lines. Each state’s take on privacy and AI governance varies, making the legal landscape difficult to map. But one thing’s clear: businesses that overlook these rules are setting themselves up for more than just a compliance headache; they’re facing potential lawsuits, fines, and a serious hit to customer trust.

Canada: A more unified approach

Canada has taken a more unified approach to AI regulation compared to the US, with a focus on creating a national framework. The proposed Artificial Intelligence and Data Act (AIDA) requires that AI systems are safe, transparent, and fair. It also requires companies to use reliable, unbiased data in their AI models to avoid discrimination and other harmful outcomes. Under AIDA, businesses must conduct thorough risk assessments and ensure their AI systems don’t pose a threat to individuals or society.

Alongside AIDA, Canada also proposes a reform of the Personal Information Protection and Electronic Documents Act (PIPEDA) which governs how businesses handle personal information. When it comes to AI, PIPEDA places strict rules on how data is collected, stored, and used. Under PIPEDA, individuals have the right to know how their personal data is being used, which presents a challenge for companies developing AI models. Businesses need to check that their AI systems are transparent, and that means being able to explain how the system makes decisions and how personal data is involved in those processes.

In June 2022, Canada introduced Bill C-27, which includes three key parts: the Consumer Privacy Protection Act (CPPA), the Personal Information and Data Protection Tribunal Act, and the Artificial Intelligence and Data Act. If passed, the CPPA would replace PIPEDA as the main privacy law for businesses. In September 2023, Minister François-Philippe Champagne announced a voluntary code to guide companies in the responsible development of generative AI systems. This code offers a temporary framework for companies to follow until official regulations are put in place, helping to build public trust in AI technologies.

Gcore: supporting compliance and innovation

Keeping artificial intelligence in step with innovation and compliance is tricky in a continuously shifting regulatory environment. Businesses must keep up to date by monitoring the changes in regulations across states, at the federal level, and even across borders. This means not just understanding these laws but embedding them into every process.

In an environment where the rules are changing from day to day, Gcore supports global AI compliance by offering localized data storage and edge AI inference. This means your data is automatically handled in full accordance with rules specific to any region or field, whether it’s healthcare, finance, or any other highly regulated industry. We understand that compliance and innovation are not mutually exclusive, and can empower your company to excel in both. Get in touch to learn how.

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