Data is the currency of scientific research. Its security should not be left to chance.

Data integrity is crucial across all forms of research, including Life Sciences research.  After all, it’s the only way researchers and regulators can assure the quality, safety, and efficacy of their products.

The way Life Science companies store and communicate data has become increasingly crucial to validating the expectation that their data is safe and secure; as a result, organizations must be hyper-vigilant in mitigating data risks like cyberattacks, data breaches, and record falsification.

In fact, these expectations have grown in recent years. As the Life Science industry grows in complexity, the use of highly automated Cloud-enabled systems makes data integrity increasingly important to sustainable success. Compliance needs are driving organizations to make their data-related processes more robust and secure.

It takes more than controls, processes, and technology to implement good data practice. Life Science research firms must adopt a wider shift towards educating for data risk mitigation and develop a culture that understands and values data integrity.

5 Key Elements of Data Integrity 

Life Science research data needs to be complete, consistent, and accurate throughout the data lifecycle. Ensuring that all original records and true copies – including source data and metadata – remain un-compromised in the Life Science environment is no small feat.  It is important to focus on five key characteristics to increase data integrity:

Attributability. Data must be attributable to a specific project or process of the specific individual who creates it. Modifications must produce an audit trail so that people can follow the path data takes through the organization.

Legibility. Data must be legible and durable. If it isn’t readable by the eye, it should be readily accessible by electronic means. Containerization, a means to support legacy software without needing to maintain legacy hardware/IT, is one way Life Science researchers maintain legibility for scientific workflow applications.

Chronology. Metadata should allow auditors to create an accurate version history. Processes that create metadata should do so in an immediate and verifiable way.

Originality. Data should retain its original format whenever possible. Verified copies should also retain original formatting and avoid arbitrary changes.

Accuracy. Data must accurately reflect the activity or task that generated it. Metrics that measure data should be standardized across platforms.

These characteristics ensure that data is complete, consistent, enduring, and available. Once Life Science research firms implement solutions that maintain data integrity, they can begin operating in more risk-intelligent ways.

Life Science Data Risks are Unique 

Several factors combine to give Life Science research a unique risk profile. While many of the threats that Life Science organizations face are the same ones faced by the commercial and government sectors, there are structural risks inherent to the way Life Science research must be carried out.

Intellectual properties in the Life Sciences are incredibly valuable. Drug formulas, medical device blueprints, and clinical data are the result of years of painstaking research. These properties may have life-changing patient impacts and the potential to generate billions of dollars in revenue. Understandably, these assets are of enormous interest to hackers, including attackers sponsored by hostile nation-states.

As relevant as is the issue of hackers, the internal risk is something to combat, as well.  Research teams often exchange sensitive information within different work streams, and among a wide range of partners. While sharing data expedites research and development, it also increases the risk of data falling into the wrong hands. Even within the field, it is important to be aware of potentially untrustworthy sources with or without malicious intent. 

Life Science organizations typically rely on a global network of suppliers for hard-to-find materials and equipment. Supply chain attacks – where attackers exploit a weak link in a trusted vendor to infiltrate organizations down the supply chain – are a dangerous and growing trend.

Mergers and acquisitions within the Life Science industry also have a tendency to increase security risks. When two companies merge, they inevitably share data in a trust-oriented environment. If both companies’ IT teams have not taken sufficient action to secure that environment first (or adopted a zero-trust model), new vulnerabilities may come to light.

Implement Cloud Security and Risk Mitigation Strategies 

Life Science researchers do not have to give up on the significant advantages that Cloud technology offers. They simply must plan for security contingencies that reflect today’s data risk environment.

Mitigating Cloud risk means establishing a robust cybersecurity policy that doesn’t simply conform to industry standards, but exceeds them. Beyond well-accepted methods like multi-factor authentication, full data encryption (in-transit and at rest) and data exfiltration add layers of protection but require adopting a more proactive stance towards security as a tenet of workplace culture. For example, it’s critical that teams manage encryption keys, fine-grain security, and network access controls internally (vs. outsourcing to the Public Cloud provider). Additionally, work-flow controls and empowered data stewards help put controls in place with reduced impact(s) to collaborative work.

In summary, every research position is also a cybersecurity position. Teaching team members to maintain data integrity ensures secure, consistent access to innovative technologies like the Cloud.

RCH Solutions is a global provider of computational science expertise, helping Life Sciences and Healthcare firms of all sizes clear the path to discovery for nearly 30 years. If you’re interesting in learning how RCH can support your goals, get in touch with us here.