Scrappy startups have few resources and little room for error while trying to establish themselves and grow. Faced with such challenges, many founders tend not to place much importance on data security during the early stages despite the scope and sensitivity of data the startup already collects.
Not treating data security with the diligence it deserves from the outset manifests itself in various ways. Here are the most common ones, as well as practical advice on how to correct each.
Weak Access Controls
Early-stage startups typically employ only a handful of people. Giving everyone unrestricted access to SaaS platforms, customer information, or production environments doesn’t seem like a concern and might even be viewed as a practical way of removing friction.
However, this may cause serious long-term consequences. Overly permissive access is a leading data breach entry point. No wonder, since it’s unclear who has access to what, and employees who move on to other roles or leave the company may retain access.
While it’s not necessary to stifle a startup by turning it into an overly restrictive bureaucracy, prudent access measures should be enforced. This includes MFA for all important accounts, role-based access controls, individual accounts rather than shared logins, and periodic permissions reviews.
Poor Employee Training
Startups generally employ more tech-savvy people than traditional small businesses. Still, that doesn’t mean employees are immune to cybersecurity threats that target humans, especially now that AI is drastically impacting the quality and quantity of attacks.
Personalization is reinvigorating phishing, and mobile social engineering is proving particularly effective at tricking employees into revealing their account credentials or even conducting money transfers.
Employee training is a must, but a one-and-done, monolithic approach isn’t ideal. It makes more sense to keep cybersecurity training sessions short, current, and impactful. Ideally, each employee should:
- Be able to spot and report the most common social engineering threats;
- Safeguard sensitive data;
- Avoid unsanctioned software;
- Treat company devices and accounts with due consideration.
Neglecting Backups
Another easily avoidable but common mistake is treating backups like just another list item to be checked off. Technically, having backups and being able to successfully recover through them are two different things.
Backups may be out of date or incomplete. They may fail to load or be affected along with core infrastructure when disaster strikes.
A robust backup plan starts with adopting a strategy like the 3-2-1 rule that results in multiple copies, at least one of which is offsite and doesn’t have internet access. It’s equally important to periodically perform restore operations during normal working conditions while noting and addressing failures.
Irresponsible Usage of AI
The scramble to adopt the latest AI technologies can harmfully impact data security. The most common form of this is when employees prompt publicly available models and include sensitive company data without realizing that these third parties may retain and reuse it. Moreover, AI tools with weak protections may leak this data.
There’s also the problem of shadow AI. Enthusiastic adopters might see sanctioned AI tools as outdated or inadequate and adopt others without considering the impact this could have on information security. Since IT doesn’t have insight into such tools, identifying their misuse and related data leaks becomes exponentially harder.
To mitigate these risks, AI tools must be thoroughly vetted and managed. Implementing a platform like nexos.ai allows organizations to establish the necessary guardrails to protect sensitive information from accidental exposure.
Leaving Internal Tools Exposed
Startups commonly adopt a wide variety of internal tools like production environments, analytics platforms, CI/CD systems, etc. Since it’s assumed that these systems will never become customer-facing, they’re given excessive permissions or used with weaker credentials.
Keeping track of the tools’ intricacies gets harder as the startup grows, and some may eventually become publicly accessible. This can lead to unauthorized access to sensitive data or even a broader breach of company infrastructure.
Internal systems need to be catalogued and monitored. Note who has access, if the access is individual or shared, and whether measures like MFA secure it. Installed software should be patched regularly, old accounts removed, and access attempts logged.
Conclusion
Securing a startup requires a proactive mindset that integrates protection into the company’s DNA from day one. Address these common pitfalls we mentioned early, so that you can build a resilient foundation that safeguards their intellectual property and earns lasting customer trust.