While much of the technology sector has faced sharp selloffs in early 2026 over fears that generative AI tools could disrupt traditional software, data services, and knowledge-based industries, cybersecurity stands out as one corner of the market where investors can potentially find shelter from AI disruption while still benefiting from the technology’s widespread adoption, according to a Wednesday research note from Wedbush Securities.

Led by veteran tech analyst Dan Ives—a longstanding and vocal AI bull—Wedbush argues that AI will act as a powerful tailwind for the cybersecurity industry over the coming years. The rapid proliferation of AI deployments is expected to dramatically increase attack frequency, success rates, and blast radius, making protection of data, endpoints, and use cases mission-critical.

“AI will be a major tailwind to the cyber security sector over the coming years as protection of use cases, data, and end points expand markedly,” the analysts wrote. “As attack frequency, success rates, and blast radius rise with AI deployments, cybersecurity becomes even more mission-critical risk management, reinforcing budget resilience in this new AI driven IT budget world.”

Gartner projects global spending on AI cybersecurity to reach $51 billion in 2026, roughly doubling from the prior year, underscoring the sector’s rapid growth trajectory. Cybersecurity remains one of the fastest-growing categories of enterprise IT spending, with budgets proving resilient even in periods of broader tech spending caution.

Why Cybersecurity Is Seen as AI-Resilient

Wedbush and other analysts view cybersecurity as a “double-edged sword” beneficiary of AI:

• Defensive upside — AI enables faster threat detection, automated response, behavioral analytics, and predictive intelligence, strengthening defenders’ capabilities.
• Offensive threat amplification — AI empowers attackers with more sophisticated phishing, automated vulnerability scanning, polymorphic malware, deepfakes for social engineering, and faster exploit development—driving demand for advanced protection.

Unlike many software categories facing potential disintermediation or pricing pressure from AI agents and automation, cybersecurity demand is expected to rise as organizations seek to secure AI systems themselves, protect expanding attack surfaces (cloud, edge, IoT, data pipelines), and comply with stricter regulations around AI governance and data privacy.

Wedbush identified three stocks as best-positioned to capitalize on this dynamic:

1. CrowdStrike — “We believe that CRWD’s position as the gold standard of cybersecurity remains firmly unchanged in the face of this software sell-off with the company’s innovative, best-in-class Falcon platform becoming increasingly effective in the modern threat landscape as AI adversaries become an incrementally larger threat for enterprises heading down the AI path.”
2. Palo Alto Networks — “AI is not displacing PANW’s value proposition, but has actually made it more relevant, not less, as it is forcing customers to consolidate vendors, improve visibility, and automate response as threats become more adaptive and effective.”
3. Zscaler — Highlighted for its cloud-native zero-trust architecture, which aligns well with securing distributed AI workloads, remote users, and cloud-based AI infrastructure.

The cybersecurity sector has shown relative resilience amid the broader 2026 tech selloff. While software stocks broadly declined on fears of AI-driven disruption, cybersecurity names have held up better, supported by sustained enterprise budget priority and visible secular growth drivers. CrowdStrike, Palo Alto Networks, and Zscaler have each outperformed the broader Nasdaq and software indices year-to-date, with CrowdStrike particularly benefiting from its Falcon platform’s reputation as a leading endpoint detection and response (EDR) solution in an era of increasingly sophisticated threats.

The sector’s resilience is further bolstered by regulatory tailwinds—stricter data privacy laws (GDPR, CCPA expansions), SEC cybersecurity disclosure rules, and emerging AI-specific governance frameworks—that force companies to increase security spending regardless of broader IT budget cycles.

However, there are still risks while Wedbush is bullish. A prolonged economic slowdown could pressure IT budgets, even in security. Competitive intensity is high, with legacy players (Fortinet, Check Point), cloud-native vendors (Cloudflare, Okta), and emerging startups all vying for share. Rapid AI advancement could also empower attackers faster than defenders in some scenarios, requiring continuous platform evolution.

However, the consensus view among analysts is that cybersecurity remains one of the most structurally advantaged sectors in the AI era. Demand is driven not by discretionary IT projects but by necessity—protecting assets, complying with regulations, and mitigating existential risks from AI-augmented threats.

As AI adoption accelerates across enterprises, cybersecurity spending is widely expected to remain resilient and likely accelerate. Wedbush’s note reinforces the emerging narrative: while AI may disrupt certain software categories, it is simultaneously creating a massive, secular growth driver for the companies tasked with securing the AI-powered world.

In the orchestra of modern commerce, the supply chain serves as the conductor’s baton, setting the rhythm, directing the flow, and ensuring harmony across the enterprise. Yet in many organizations, that baton has become weighed down by inefficiencies: warehouses filled with slow-moving stock, lead times stretched endlessly, and coordination across partners that struggles to keep pace with demand.

A lean supply chain represents a deliberate redesign. It is the discipline of eliminating waste, synchronizing processes, and delivering value with precision. Drawing inspiration from Toyota’s philosophy of lean production, it is about achieving more with less (less time, less inventory, and lower cost) while ensuring that the customer consistently receives greater value.

But lean is not simply about trimming excess; it is about building operational strength. Organizations that embrace it optimize sourcing, digitize logistics, and use data visibility to anticipate demand. They move from reactive replenishment to proactive planning, aligning resources closely with actual consumption patterns.

In Africa, where logistics constraints remain a major barrier to competitiveness, lean supply chain models offer a pathway to reduce the inefficiencies that inflate prices and erode margins. Properly implemented, they can transform fragmented ecosystems into coordinated networks where goods move predictably, efficiently, and at scale.

Ultimately, lean supply chain is both a strategy and a mindset. It calls on firms to see partners not as entities to be pressured, but as collaborators within a shared value system. When businesses commit to lean principles, they unlock capital, respond faster to markets, and build resilience against shocks. The reward is speed, adaptability, and enduring competitiveness.

From NATO today, we will learn from a Zen-master of lean at Tekedia Mini-MBA.

Thur, Feb 19 | 7pm-8pm WAT | Lean Supply Chain Applications in Business – Chibueze Noshiri, NATO Luxembourg  | Zoom link

The European Commission is probing whether Shein’s gamified features, recommendation systems, and safeguards against illegal products comply with the Digital Services Act.

The European Union has intensified regulatory pressure on Shein, launching a formal investigation under the bloc’s sweeping Digital Services Act (DSA) over what it describes as “addictive design,” opaque recommender systems, and the potential sale of illegal products.

In a statement issued Tuesday, the European Commission said it will examine whether elements of Shein’s platform architecture and risk controls comply with the DSA’s obligations for large online platforms. The move signals a broader regulatory shift in Europe: scrutiny is expanding beyond pricing and consumer transparency to the very design mechanics that drive user engagement and sales.

At the center of the probe is the Commission’s concern that Shein’s interface may encourage compulsive engagement through gamified rewards, point systems, and time-sensitive incentives. Regulators argue that such mechanisms could undermine consumer well-being by nudging users toward repeated or impulsive purchases.

Under the DSA, platforms must assess and mitigate systemic risks stemming from their design choices, including how recommender systems amplify certain content or products. Transparency obligations require companies to explain how algorithms prioritize listings and to provide meaningful information about how recommendations are generated.

The Commission said it will examine whether Shein’s recommender systems meet those transparency standards. The investigation also extends to the company’s systems for preventing illegal listings, including products described as “child-like sex dolls,” which would be prohibited under EU law.

If violations are confirmed, the Commission can impose fines of up to 6% of global annual turnover and require structural changes to platform operations.

French Action and Broader EU Consumer Concerns

The EU-level investigation follows earlier national action. In 2024, the French government sought to suspend Shein’s website over reports of sex doll listings. A Paris court rejected the suspension request in December but ordered the company to implement age verification measures for adult products and imposed financial penalties for non-compliance.

Shein has also faced scrutiny from EU authorities over marketing practices. In May, the bloc accused the company of misleading customers through fake discounts, inadequate refund processing, and obscured customer service contact information — practices it said breached EU consumer protection rules.

Together, the actions suggest regulators are examining Shein’s operations across multiple fronts: product compliance, marketing transparency, and digital design.

Supply Chain, Safety, and International Investigations

Outside Europe, Shein’s regulatory exposure continues to grow.

South Korean authorities have previously reported detecting toxic substances in some of the company’s products above legal thresholds. In the United States, a Texas court said in December it would investigate Shein for “unethical labor practices and the sale of unsafe consumer products.”

Ken Paxton, the Texas Attorney General, said at the time: “Any company that cuts corners on labor standards or product safety, especially those operating in foreign nations like China, will be held accountable.”

The convergence of consumer safety, labor practice, and digital governance investigations reflects mounting global pressure on ultra-fast fashion models that rely on rapid product turnover and algorithm-driven merchandising.

It’s High Stakes for Shein

The DSA probe arrives at a sensitive time for Shein, which has been expanding aggressively in Europe while navigating regulatory and reputational headwinds.

Europe represents a significant growth market for the company. Compliance failures under the DSA could result not only in fines but also in mandated operational adjustments that affect how Shein structures promotions, gamification, and product recommendations — core components of its high-frequency sales model.

The Commission’s focus on “addictive design” highlights a broader regulatory trend in the EU toward scrutinizing behavioral design techniques, sometimes referred to as “dark patterns.” Policymakers increasingly view digital interface choices as capable of influencing economic behavior at scale.

If regulators determine that Shein’s engagement mechanisms constitute systemic risk, the case could establish precedent for how the EU applies the DSA to e-commerce platforms, not just social media networks.

A Test Case for the Digital Services Act

The Digital Services Act, fully applicable to large online platforms since 2024, represents one of the EU’s most ambitious digital governance frameworks. It imposes obligations on platforms to assess and mitigate risks related to illegal content, consumer harm, and public safety, and to ensure algorithmic transparency.

By invoking the DSA against Shein, the Commission is signaling that retail marketplaces with heavy algorithmic personalization fall squarely within the scope of systemic risk oversight.

More than 50% of the software enterprises currently purchased could be replaced by artificial intelligence systems, according to Arthur Mensch, chief executive of Mistral AI, adding fuel to investor anxiety over the durability of traditional software business models.

Speaking to CNBC at the India Accelerates event on the sidelines of the AI Impact Summit in New Delhi, Mensch said: “I would say more than half of what’s currently being bought by IT in terms of SaaS is going to shift to AI.”

His comments come amid a sharp correction in software stocks, partly triggered by advances in enterprise-focused AI tools such as Anthropic’s Cowork product. Investors increasingly worry that generative AI systems can replicate — or outperform — many of the functions currently delivered by subscription-based software platforms.

The iShares Expanded Tech-Software Sector ETF, which includes major holdings such as Microsoft and Salesforce, has fallen more than 20% this year. In India, software giants such as Tata Consultancy Services and Infosys have also declined, reflecting broader concerns that AI may compress demand for legacy enterprise solutions.

The “Replatforming” Argument

Mensch framed the shift not as incremental substitution but as structural replatforming. He argued that AI systems can now generate custom applications within days — replacing what once required specialized vertical SaaS vendors.

“AI is making us able to develop software at the speed of light,” he said.

According to Mensch, when enterprises have the appropriate infrastructure — particularly the ability to connect internal data securely to AI systems — they can rapidly build workflow applications for procurement, supply chain management, and other operational processes.

“Five years ago, you would actually need a vertical SaaS,” he said. Now, AI tools can create tailored workflow software in a matter of days.

The implication is that companies may bypass traditional software vendors entirely, instead using foundation models and AI development platforms to build internal tools.

Mensch described a “replatforming” trend in which enterprises reassess decades-old IT systems. He said Mistral now has more than 100 enterprise customers exploring the replacement of older software stacks, particularly where licensing costs have escalated.

“They see AI as a way to replatform the thing so that it becomes more efficient and less costly,” he said.

Mensch drew a distinction between workflow software — which he sees as vulnerable — and systems of record, which he believes will remain foundational.

Systems of record manage core enterprise data, including financial ledgers, HR records, and customer databases. These platforms often serve as the backbone of corporate IT architecture and are deeply embedded in compliance and operational processes.

“Systems of records are not going to change,” Mensch said, suggesting AI will sit on top of these databases rather than replace them.

Bipul Sinha, CEO of Rubrik, echoed that view in a separate CNBC interview, arguing that workflow software could face significant disruption, while data infrastructure enabling AI could benefit from increased demand.

This distinction is central to investor strategy. If AI primarily replaces user-interface-driven workflow tools while reinforcing demand for data storage, governance, and cloud infrastructure, then the winners and losers within the software sector may diverge sharply.

Market Implications for SaaS Models

The software-as-a-service model has long relied on predictable subscription revenue, high gross margins, and long-term contracts. If enterprises shift from buying packaged software to building AI-driven custom applications, that revenue model could come under pressure.

Instead of paying recurring fees for standardized tools, companies might invest in AI infrastructure and in-house development talent. Over time, this could reduce vendor lock-in and increase price sensitivity.

The speed of AI-driven application development also challenges the traditional sales cycle. If a company can prototype and deploy an internal workflow tool in days, it may be less inclined to negotiate multi-year contracts with external SaaS providers.

Investors are grappling with whether AI represents incremental enhancement — embedded within existing platforms — or a fundamental threat to the SaaS ecosystem.

Mistral’s India Expansion

Mensch also outlined plans for Mistral AI to open its first office in India this year. The move signals the company’s intent to compete in one of the fastest-growing AI markets globally.

While Mistral is building its own data centers in Europe, its India strategy will rely on partnerships with firms that already operate local infrastructure. This approach reflects regulatory sensitivities around data sovereignty, as India encourages AI providers to ensure domestic data storage and local processing capabilities.

Mensch said Mistral is already working with multinational firms that have operations in India and is now actively pursuing public- and private-sector customers based in the country.

India’s linguistic diversity, including languages such as Hindi and Punjabi, presents both a technical challenge and a commercial opportunity. Mistral’s large language models are designed to accommodate multilingual inputs, which Mensch described as crucial for long-term consumer adoption.

“That’s something that down the line will be super important for the Indian consumer market,” he said.

Mistral, founded in France, positions itself as a European alternative to U.S.-based AI giants such as Anthropic and OpenAI. Its expansion into India underscores the intensifying global competition for enterprise AI customers.

As enterprises weigh whether to integrate AI into existing SaaS platforms or rearchitect systems around AI-native tools, the debate over software displacement is likely to intensify.

Mensch’s assertion that more than half of enterprise SaaS spending could shift to AI may be contested. But the underlying trend, rapid AI-driven customization, workflow automation, and infrastructure investment, is reshaping how investors evaluate software companies.