Cybersecurity is a critical concern for startups and growing businesses, especially as digital transformation accelerates. With more companies relying on cloud services, remote work and AI-driven tools, cyberthreats evolve at an alarming pace. One often-overlooked way to strengthen cybersecurity is by building diverse IT teams.

Teams with different experiences and problem-solving approaches bring fresh perspectives that help identify risks, improve threat detection and develop stronger defence strategies. Prioritising diversity in IT teams can build a more resilient framework, which reduces risks and ensures long-term success.

Broader perspective on cyberthreats

Diverse teams bring a wealth of perspectives that strengthen cybersecurity defences, helping enterprises anticipate and respond to a wide range of threats. Cybercriminals don’t operate with a one-size-fits-all approach. They adapt their tactics based on regions, industries and cultural behaviours, but many teams lack the diversity needed to recognise these evolving risks.

In Southeast Asia, for example, women make up only 34 per cent to 40 per cent of the tech workforce, which leaves a significant gap in representation. A more balanced team brings different ways of think, allowing brands to identify blind spots a homogenous group might miss.

Employees from different backgrounds and sectors can spot vulnerabilities others might overlook, providing a well-rounded approach to threat detection. Someone with experience in financial services may recognise banking-specific phishing scams, while a team member from the health care sector may be more aware of medical data breaches. This diversity in thought and experience leads to stronger problem-solving, more innovative security strategies, and a cybersecurity framework that evolves alongside emerging threats.

Improved problem-solving

Diversity fuels creative problem-solving and enables teams to think beyond traditional defense strategies. Cyberthreats constantly evolve, so a one-dimensional approach often falls short against sophisticated attacks. When the staff comes from different backgrounds, industries and cultures, they bring unique insights that help uncover unconventional solutions.

Cyberattacks require quick thinking and adaptability, and a team with varied experiences is more likely to approach problems from multiple angles. This diversity in thought reduces blind spots, strengthens risk assessment and improves overall resilience.

Also Read: What if cybersecurity included everyone it protects?

Increased innovation in cybersecurity tools and strategies

A cybersecurity team with diverse technical skills and perspectives is far better equipped to develop cutting-edge security solutions, especially as cyberthreats become more sophisticated. AI-powered attacks are dynamic, making traditional defence strategies less effective. However, many organisations face a major obstacle — “insufficient personnel to manage tools and alerts” is one of the biggest challenges in defending against AI-driven threats.

Without enough skilled professionals, security teams struggle to analyse threats in real time, leaving their employers vulnerable to breaches. A diverse IT team helps bridge this gap by bringing in expertise from varying fields, which ensures a well-rounded approach to cybersecurity. Exposure to various security frameworks and technologies also fosters innovation to help businesses stay ahead of emerging threats.

For example, a professional with cloud security expertise might identify risks a network security specialist could miss. Meanwhile, those with AI and machine learning backgrounds can enhance automation in threat detection and response. When cybersecurity professionals from multiple disciplines collaborate, they create a more adaptive and proactive defence strategy.

Enhanced understanding of social engineering attacks

Cybercriminals constantly refine their tactics, often using cultural and psychological manipulation to exploit human behaviour in phishing scams and fraud. Scammers tailor their messages to different regions with language, social norms and local events, tricking victims into clicking malicious links or revealing sensitive data.

In 2023, the US sent eight billion spam emails in a single day, highlighting the sheer scale of phishing threats. With cybercriminals deploying region-specific scams, brands must take a proactive approach to security hat accounts for cultural nuances and evolving attack patterns.

A diverse IT team is better equipped to recognise and mitigate these targeted threats. Employees with different linguistic skills and cultural insights can identify red flags in tone, structure or context that align with specific regional fraud tactics. Whether spotting financial scams in Southeast Asia or fake tech support calls in India, a well-rounded team ensures stronger protection across multiple markets.

Also Read: How an AI cybersecurity company harnesses the power of AI for optimal business performance

Stronger compliance with international cybersecurity regulations

Companies operating across borders face the challenge of navigating multiple data protection laws, each with its own set of regulations and penalties. Compliance is not optional — failing to meet legal requirements can lead to hefty fines and reputational damage. In Singapore, for instance, people can incur penalties of up to SG$1 million (US$747,097.87 approx.) for noncompliance with the Personal Data Protection Act. This underscores the severe consequences of data breaches and mishandling of personal information.

A diverse IT team can manage these compliance challenges. Professionals with experience in different regions bring valuable insights into global data protection laws, ensuring the enterprise adheres to multiple regulatory standards. Fostering diversity can reduce legal risks, strengthen data governance and build trust with customers across global markets.

Stronger cybersecurity leads to innovation and trust

Building a diverse IT team goes beyond meeting compliance requirements. It fosters innovation, strengthens cybersecurity strategies, and builds trust with customers and stakeholders. Embracing diversity can develop more adaptive security solutions, enhance threat detection, and create a resilient digital infrastructure that protects data and reputation.

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The US$1.5 billion hack of a major crypto exchange last month — possibly the largest digital theft ever — has once again revealed the weak spots in exchange security. And the timing couldn’t be worse. Bitcoin was hitting record highs, institutional investors are diving in, and mainstream adoption is accelerating. Yet, for all its progress, crypto remains dangerously vulnerable.

As digital assets shift from speculative investments to core components of the financial system, the industry has reached a turning point. The question now isn’t whether crypto will change finance, but whether exchanges can earn the trust needed to make that shift a reality.

Moving beyond quick fixes

Following the US$1.5 billion hack, Bitcoin dropped below US$80,000, and investor sentiment indices swung dramatically from “extreme greed” to “extreme fear” — erasing billions in market value virtually overnight.

This volatility exposes an uncomfortable truth: many exchanges continue operating with security frameworks designed for crypto’s early days, when stakes and attack sophistication were considerably lower. These approaches typically prioritise technological solutions while neglecting the equally important human element.

The Financial Action Task Force (FATF) recently highlighted how regulatory gaps create exploitable loopholes within the crypto ecosystem. These vulnerabilities demand urgent attention from both regulators and industry participants who genuinely care about the sector’s long-term viability.

Also Read: Why AI security demands a different playbook in Asia

Three pillars of next-generation security

Tomorrow’s exchanges must build security frameworks on three fundamental pillars:

• Multi-Layered Technical Infrastructure

Security must extend beyond basic key management to include comprehensive threat detection, real-time monitoring, and automated circuit breakers capable of halting suspicious transactions before they complete. Prevention, not just detection, needs to become the industry standard.

• Human-Centric Security Protocols

Most significant breaches begin with social engineering rather than technical vulnerabilities. Exchanges must implement rigorous staff training, rules-based access controls, and zero-trust frameworks that limit potential damage from compromised accounts or insider threats.

• Transparent Asset Management

Users deserve verifiable proof that their assets are secure. This involves conducting regular third-party audits, offering real-time proof of reserves, and providing 1:1 asset backing guarantees that can be independently verified at any time.

At progressive exchanges, this comprehensive strategy integrates technologies such as multi-party computation (MPC), cold storage custody, and enterprise-level encryption to enhance security and transparency. Equally important is maintaining platform independence by ensuring no customer funds are stored on external exchanges, significantly reducing potential attack vectors.

Regulatory engagement as competitive advantage

As regulatory frameworks evolve unevenly across different regions, forward-thinking exchanges should see compliance not as a challenge, but as a competitive advantage.

Asia is leading the way in thoughtful crypto regulation, with Singapore and Hong Kong offering balanced models that protect consumers while fostering innovation. Their approaches show that regulations can support, rather than hinder, the growth of the industry.

Also Read: Your job is not your safety net: Build your own security

Proactive compliance isn’t about mere box-ticking but building systems aligned with traditional financial protections while accommodating digital assets’ unique characteristics. This means going beyond minimum requirements to establish robust anti-money laundering (AML) / know-your-customer (KYC) processes, maintaining clear separation between client and operational funds, and creating transparent governance structures.

Rebuilding trust through education

Beyond technical and regulatory concerns, the most important factor is rebuilding user trust through education and empowerment.

Even the most robust security measures are useless if users don’t understand how to use them or aren’t aware of their importance. Exchanges must focus on creating user-friendly designs that make security straightforward, not a burden. They should also provide clear instructions and tools that help users manage their own security effectively.

As crypto adoption grows beyond experienced traders to the general public, exchanges need to strike a balance. They must offer strong security without making it complicated or frustrating for everyday users.

The path forward

The recent US$1.5 billion hack represents both a crisis and an opportunity. Those who don’t learn from it risk being left behind, while those who rise to the challenge have the chance to set new industry standards.

As the industry matures, security can no longer be seen as just a technical hurdle; it must become the bedrock of the entire crypto ecosystem. Security goes beyond protecting assets to safeguarding the vision of a more accessible and efficient financial system.

Technological solutions will keep evolving, but the real change will come from a shift in culture—putting user protection at the heart of every decision and creating systems where security is built in from the start, not tacked on later.

By adopting this approach, crypto exchanges can not only survive current challenges but also help drive the future of finance toward greater security and transparency. The industry’s long-term success won’t come from speculative booms but from earning and keeping the trust of users everywhere.

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The advent of emerging technologies such as automation, artificial intelligence (AI) and machine learning (ML) has drastically disrupted what it takes to remain relevant. Globally, enterprises are seeking strategies that can provide them with a competitive edge in a rapidly evolving digital landscape.  According to a recent report, the global hybrid cloud market is expected to reach US$262 billion in 2027, with the APAC region expected to grow at the highest rate.

Hybrid cloud strategies have gained popularity as they provide the flexibility, scalability, and redundancy businesses need for improved modern operations. By integrating the benefits of both public and private clouds, businesses can utilise the scalability of public cloud services while keeping sensitive data and applications securely within their private cloud infrastructure.

Notwithstanding the numerous financial and efficiency benefits of hybrid cloud solutions, a lack of proper data management practices can quickly manifest into significant cybersecurity hurdles. With widespread cloud adoption, organisations are exposed to increased cyber risks and are also introducing complexity into their operations.

Notably, cloud-related threats are among the top three cyber concerns for 51 per cent of APAC organsations. Hence, finding the right balance between leveraging hybrid cloud benefits and maintaining a secure network for cyber resilience is crucial. So how can businesses best pursue their hybrid cloud set up, while ensuring that their network remains secure?

New multi-cloud cyber challenges raining on businesses

Managing a multi-cloud ecosystem without effective data practices can hinder an organisation’s ability to implement sound security frameworks.

Clear data visibility is critical in enabling an organisation’s capacity to integrate frameworks, such as zero trust. In particular, zero trust frameworks operate on the principle that no entity, whether inside or outside the network, should be trusted by default, necessitating continuous monitoring and verification of every request. The ability to identify whether a user is verified or not can become complicated if an organisation does not have adequate oversight of their data.

Ensuring data is protected whilst it moves between public and private clouds is another significant challenge. The movement of data across different environments can often increase the risk of exposure to malicious actors. Robust encryption and secure communication channels are essential to protect data during transit and storage.

Also Read: How cybersecurity teams can involve HR to optimise incident response

For many, taking the first step in implementing security solutions across various cloud environments can be daunting, but it is increasingly necessary. Australia, in particular, faces heightened risks, with the new statistics from the Office of the Australian Information Commissioner (OAIC) showing the number of data breaches notified to the regulator in the first half of 2024 was at its highest in three and a half years.

Recent data breaches from high-profile organisations across the region proves just how critical strong data coordination is for security purposes. Business leaders must ensure that their security measures are uniformly applied across both public and private clouds to prevent such vulnerabilities and risks.

Strategies for enhanced security in hybrid cloud environments

To navigate the cybersecurity challenges posed by hybrid cloud strategies, businesses of any size should look to implement several key measures:

• Perform cyber risk assessments

Conducting thorough cyber risk assessments can help organisations understand how their data is stored and identify potential vulnerabilities. Regular reviews can enable businesses to stay ahead of potential threats and implement necessary security measures proactively.

• Implement Zero Trust Frameworks

Adopting zero trust frameworks ensures that malicious activities are detected promptly. By continuously monitoring and verifying every access request, organisations can prevent unauthorised access and reduce the risk of data breaches.

• Utilise robust encryption

Robust encryption protects data both in transit and at rest. By encrypting data, businesses can secure sensitive information and prevent unauthorised access, even if data is intercepted during transmission between public and private clouds.

Also Read: Embracing AI evolution: The crucial role of data management and cybersecurity in AI success

• Employ multi-factor authentication (MFA)

MFA adds an extra layer of security by requiring multiple forms of verification before granting access to critical or sensitive data. This significantly reduces the risk of unauthorised access, as attackers would need to bypass multiple authentication factors.

• Adhere to the principle of least privilege

The principle of least privilege ensures that users are granted the minimum level of access necessary to perform their tasks. By limiting access rights, businesses can minimise the potential damage caused by compromised accounts and prevent unauthorised access to sensitive data.

• Enhance security posture with AI capabilities

The integration of AI capabilities into cloud data protection and security solutions will significantly enhance efficiency. These tools not only alleviate tedious tasks but also swiftly identify patterns, trends and anomalies that might otherwise be undetected and autonomously adjust security parameters—such as multifactor authentication and multi-person authentication—to lock down access to data and protect it against attacks.

Hybrid cloud strategies are opening new doors for businesses looking to keep their competitive edge in a rapidly evolving market. Done right, businesses can optimise their operations by combining the flexibility of public clouds with the security and control of private clouds.

Embracing these new pathways necessitate proactive data monitoring and management. Effective data management, with expanded AI-driven capabilities to strengthen cyber resilience, is essential to fully harness the benefits of a hybrid cloud environment while mitigating potential risks.

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AI adoption across Asia is exploding. The region is now second only to North America in generative AI implementation, with spending projected to reach US$110 billion by 2028.

From tech giants in South Korea to manufacturers in Japan and finance firms in Singapore, AI is being rapidly integrated across key sectors.

Yet with this growth comes risk. Organisations aren’t just facing cyber threats anymore. They’re confronting something new and sneaky: adversarial threats specific to AI systems. These threats bypass traditional (cyber)security tools and expose fundamental weaknesses in how AI models are designed, used, and governed.

That’s where AI security comes in. And it’s not the same as cybersecurity.

Traditional cybersecurity tools can’t stop AI threats

AI security focuses on defending AI systems from manipulation. This includes input tampering, training data poisoning, and jailbreak prompts that exploit model behaviour, all without needing to breach a firewall or exploit a software bug.

Take prompt injection, for instance. An attacker can craft a seemingly harmless message that causes a chatbot to reveal sensitive data or bypass its guardrails. Unlike malware or phishing, these attacks work by exploiting the model’s helpfulness, not its vulnerabilities.

Hence, a starkly different attack approach in both scenarios:

Traditional cybersecurity simply isn’t designed to handle AI manipulation attacks. Legacy systems rely on rule-based detections, static infrastructure monitoring, and code-centric threat models.

AI threats move faster, scale wider, and morph with every prompt. The unfortunate outcome of this is that even the best-defended networks can become vulnerable when AI models are exposed.

Asia’s AI threat landscape

Nowhere is this gap more urgent than in Asia. The region’s proximity to China, home to some of the world’s most advanced and affordable AI models such as DeepSeek R1, Baidu ERNIE Series, and Alibaba QWEN Models, creates both opportunity and exposure.

Also Read: How my entrepreneurial failures led me to rethink learning and upskilling

China’s AI tools are increasingly used across borders, yet data stored or processed under Chinese law carries heightened regulatory and espionage risks.

Meanwhile, countries like Singapore, India, Japan, and South Korea are racing to implement AI in every corner of the enterprise. But fast adoption has outpaced governance. Shadow AI—the use of unauthorised AI tools by employees—has surged.

Consider these real-world examples:

• Samsung chip data leak: In May 2023, Samsung engineers leaked sensitive chip data by pasting code into ChatGPT to troubleshoot. Unaware (or ignoring, who knows?) that inputs could be retained and used to train the model, they exposed proprietary information outside company oversight—a clear case of Shadow AI. Samsung responded by banning external AI tools and began developing internal alternatives.
• GitHub copilot leak: A caching flaw in GitHub Copilot exposed private code snippets to unintended users. Over 16,000 organisations, including major firms in Asia, were affected. Leaked content included proprietary logic, API keys, and unreleased features. No breach happened, just AI mishandling sensitive data. It’s a sobering example of how AI systems can create security risks without traditional hacking.

These threats aren’t hypothetical. They’re already impacting some of Asia’s most advanced companies.

Shadow AI: The silent breach happening inside Asian enterprises

Shadow AI is the unauthorised use of AI tools outside the purview of IT or security teams. It’s exploding in Asia’s fast-moving economies, where employees turn to tools like ChatGPT, Gemini, or Copilot to move faster and meet tight deadlines.

Here’s the problem:

• 38 per cent of employees of 7,000 employees surveyed admit to sharing confidential data with AI tools without IT approval.
• From March 2023 to March 2024, there was a 485 per cent spike in sensitive data input into unauthorised AI applications.
• In fact, 27.4 per cent of data inputted into AI tools is considered sensitive.
• And according to IBM, breaches involving shadow AI took an average of 291 days to identify and contain, significantly longer than traditional breaches, resulting in higher costs averaging US$5.27 million per incident.

In places like Singapore, where 66 per cent of businesses say they’re not moving fast enough with AI, the temptation to bypass governance is even higher. Combine that with light-touch regulation in Japan, regulatory gaps in India, and regional competitive pressure, and you get a region-wide surge in invisible risk.

Actionable steps to mitigate AI security risks

Here’s how to assume a better AI security posture in the midst of these risks:

Real-time AI monitoring

You can’t protect what you can’t see. Deploy tools that continuously monitor how AI models are used, what inputs they receive, and what outputs they generate. This is especially critical for detecting prompt injection and data drift that legacy logging won’t catch.

Examples include model observability platforms that track prediction anomalies, latency shifts, and suspicious prompt behaviour in real-time.

Also Read: Levelling the playing field: How AI can transform SME hiring

Shadow AI governance

Catalog all AI tools in use—approved or not. Create an “AI Bill of Materials” to track model versions, data access points, and usage patterns. Block unsanctioned tools at the firewall or via endpoint controls.

Train employees on what’s allowed and why it matters. 90 per cent of shadow AI use comes from non-corporate accounts. That’s a policy failure, not just a technical one.

Token and API hygiene

Manage API tokens like you would encryption keys. Use expiration windows, rotating credentials, and revocation capabilities. Apply least-privilege principles and prevent token reuse across multiple AI environments.

APIs are the connective tissue of AI systems. If compromised, they become the fastest path to your most sensitive models and data.

AI-specific security frameworks

Don’t retrofit existing policies. Adopt AI-native frameworks that account for:

• Adversarial prompt testing
• Output validation pipelines
• Role-based model access
• Immutable audit trails for training data

Zero Trust principles apply here: Never trust an input, always verify an output.

The patchwork of AI regulations in Asia you can’t ignore

Asia’s data protection landscape is maturing fast, but remains fragmented. Some highlights:

• Singapore’s PDPA mandates consent and breach reporting, but excludes anonymised data.
• India’s DPDP Act (2023) imposes consent, localisation, and penalties up to US$6 million.
• Japan’s APPI applies globally to anyone processing Japanese citizens’ data.
• China’s PIPL is one of the strictest globally, with limits on cross-border transfers and heavy audit requirements.

More laws are coming. South Korea now regulates high-risk AI. Japan is drafting a Basic Law for Responsible AI. And China is moving toward regulating critical AI systems under national security concerns.

If you operate across Asia, this means:

• Higher compliance costs
• More explainability and audit requirements
• Tighter controls on sensitive data and cross-border transfers

Also Read: Breaking barriers: Empowering women in entrepreneurship with AI and automation

Final thoughts

Cybersecurity protects your perimeter. AI security protects your future. These are not the same job.

If you’re investing in generative AI, you’re already in the risk zone. And if you’re in Asia, that risk is magnified by regulatory ambiguity, workforce behaviour, and geopolitical complexity.

Now is the time to:

• Benchmark your AI risk surface
• Monitor models continuously
• Govern usage at every layer
• Build policies specifically for AI

AI is transforming Asia’s economy. But without AI security, it may just as easily transform into its biggest liability.

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Editor’s note: e27 aims to foster thought leadership by publishing views from the community. Share your opinion by submitting an article, video, podcast, or infographic.

Join us on Instagram, Facebook, X, and LinkedIn to stay connected. We’re building the most useful WA community for founders and enablers. Join here and be part of it.

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In the past month, I built more products than I would typically ship in a year.

Not mockups. Not pitch decks. Not “coming soon” landing pages. Actual working MVPs.

I didn’t suddenly hire a 50-person engineering team. I didn’t discover a secret growth hack.

I started vibe coding: consciously, deliberately, and with full awareness of the risks.

From “landing page MVPs” to real MVPs

Historically, when founders wanted to test an idea quickly, we launched a landing page.

Collect emails. Measure interest. Then maybe build the product.

But here’s what changed for me this year: With AI-assisted development, the time it takes to build a landing page is now roughly the same time it takes to build a usable MVP.

In the last month alone, I shipped or progressed multiple products concurrently, including:

• Seraphina AI
• Cultural and community platforms
• Games and identity-based apps
• Marketplace and advertising tools

Each of these would normally take 8-10 weeks minimum to reach a first usable version. Some would’ve taken months. And there’s no way I could’ve done them in parallel as a solo founder before.

Vibe coding isn’t “no-code” — It’s founder-led development

Let’s be clear: Vibe coding doesn’t mean “type vibes, ship magic.”

It means:

• I still design the system architecture
• I still create flowcharts and briefs
• I still review code
• I still handle security considerations
• I still document everything

Also Read: Vibe coding: Why Singapore needs more tech built for joy, not just utility

The difference?

I’m briefing an AI the same way I brief my engineers.

If you don’t understand systems, architecture, or product logic, this is dangerous. But that’s true whether your code is written by AI or humans.

Everything is dangerous if you don’t know what you’re doing.

Speed didn’t remove judgment — it amplified it

What surprised me most wasn’t speed. It was agency.

I no longer need to wait weeks just to see if something can exist. I can now go from: Idea → MVP → first dollar → decision … in a single cycle.

This doesn’t eliminate developers. It eliminates mindless iteration, waiting, and guesswork.

The bottleneck is no longer execution. It’s judgment.

The hidden risk most people miss

AI doesn’t magically produce clean code.

Just like human-written code, everything bloats if you’re not disciplined.

“Just because it works doesn’t mean it’s clean or scalable.”

The difference now is that founders who understand:

• Systems
• Product flows
• Real customer feedback

… can iterate faster with ownership.

You can export the code. You can refactor it. You can scale it properly.

The risk isn’t vibe coding. The risk is that founders who think they can skip thinking.

Also Read: The Agency: AI-augmented development in action

What shouldn’t be vibe-coded?

If a system already exists — refined by years of real customer feedback — don’t rebuild it.

I still use my own mature platforms for funnels and operations because you can’t vibe-code lived experience.

AI accelerates new ground. It doesn’t replace battle-tested systems.

The real shift isn’t technical — it’s cognitive

AI didn’t make me lazier. It made me more articulate.

The more I interact with AI, the better I’ve become at:

• Explaining intent
• Questioning assumptions
• Clarifying logic
• Thinking in systems

We’re not losing thinking skills.

We’re being forced to think more clearly.

A new year, a new baseline

Vibe coding isn’t a trend. It’s not a shortcut. It’s not a replacement for experience.

It’s a new baseline for founders who evolve with the tools.

If you adapt to change, you won’t be replaced. If you don’t — history has already answered that question.

2026 won’t reward the fastest typers. It will reward founders with clarity, judgment, and the courage to build in public — faster than ever before.

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