How to think about information security for SaaS companies
Delivering digital services in the cloud is very much a trust business, especially for SaaS companies offering services that store personal data. The trust between provider and customer is crucial for the business to work. This requires SaaS companies to prioritise information security. How can your company increase security and minimise risks for your customers?
High demands on information security
In general, the SaaS industry is strong when it comes to cybersecurity. Applications are built with a forward-looking security architecture, with zero trust, identity management and multi-factor authentication as core principles. But a more serious security situation and an ever-changing threat landscape have brought security to the fore.
As an operator in the SaaS industry, your organisation not only faces traditional and evolving security challenges. You are also subject to a range of regulations and laws related to cyber and information security - including the updated EU NIS2 directive.
“Working with data integrity and data segregation are two ways to build fail-safe principles into system design”
Additional layers of security
A vulnerable and changing security landscape, combined with user data often stored beyond the SaaS companies' boundaries, means the industry needs to adopt additional layers of security protection. Working with data integrity and data segregation, i.e. ensuring that your data remains accurate and unchanged, and that different users' data is kept separate, are two critical elements to prevent breaches and minimise the risk of information falling into the wrong hands.
Data integrity and data segregation are two ways to build so-called ‘fail-safe’ principles into system design. These security and design principles prevent and identify problems and limit the impact on both users and systems should something go wrong. By setting a clear boundary between users' data, you can separate content between accounts and limit the damage in the event of a data breach. Other examples of fail-safe principles include implementing end-to-end encryption for sensitive communications and ensuring that encryption keys are securely managed and automatically backed up. Or storing backups in separate locations to protect against data loss in case of disasters. Read more att digg.se
“Investing in security services based on automation and AI is a good complement to in-house security expertise or external security specialists and SOC”
Boosting security with AI
AI and machine learning have become an integral part of the future of security, especially for SaaS companies. Investing in security services based on automation and AI is a good complement to in-house security expertise or external security specialists and SOCs.
AI-based security systems analyse huge amounts of data in real time and can quickly identify anomalies and potential threats. This enables proactive protection and faster response to incidents. For SaaS companies, this means:
- Improved threat detection. By analysing behaviour and anomalies with AI, you can identify advanced and previously unknown threats.
- Automated incident response. Isolate infected systems and initiate countermeasures quickly to minimise the damage of an attack.
- Continuous monitoring. AI enables round-the-clock monitoring of networks and systems, which is crucial in a global, constantly connected environment.
- Efficient use of resources. Automating routine tasks frees up your security team to focus on more complex problems.
“There is really no such thing as good enough protection today, the risk of being breached with data going astray is something we have to learn to live with”
However, the increased use of AI in security work raises new questions about responsibilities, regulation and legal requirements for the secure handling of data. Therefore, as a SaaS company, you should keep an eye on:
- Data integrity and confidentiality. This means that AI systems handle sensitive data in accordance with applicable laws and regulations.
- Transparency. Understand how an AI tool makes decisions to justify actions and maintain accountability.
- Continuous updating. AI models need to be regularly trained on new data to remain effective against new threats.
In an increasingly complex security landscape, it's crucial that you stay up-to-date on how information is protected, where data is stored, and how AI can be made more effective while maintaining your organisation's security and data integrity.
Proactive security work
There is really no protection that is good enough today, the risk of being exposed to a breach with data that goes astray is something we must learn to live with. Scenario planning and developing a clear plan for risk and incident management that is anchored in the organisation is the basis of proactive security work. This means continuously identifying vulnerabilities, analysing and managing potential threats to the company's services and data. It also means developing a clear process for detecting, understanding and responding to security incidents. After which you can implement preventive and corrective measures.
Working with an experienced security partner is crucial to navigating this rapidly changing environment. Nordlo has both a high level of expertise in information security and experience of working closely with companies in the SaaS industry. We can help you ensure robust protection of your company and customer data.
This is how we can help your business with information security