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Data warehouses and data lakes combine several different tasks. Multiple sources of data are transformed, and various functions (whether people or software) process the results using varied techniques and goals. Any security project becomes challenging since many elements, steps, and procedures could go wrong. Particularly in data lakes, the data may be incredibly adaptable. There are several different types of structured, semi-structured, and unstructured data.
Information protection measures must be taken to ensure that only authorized individuals can access data warehouses. Security for data warehouses should include strict user access controls to ensure that warehouse users can only access the data they need to accomplish their duties through Data Mining Services, data lake engineering services, enterprise data lake engineering services, and data science services.
Here are the most common security procedures for data warehouses and cloud data lakes, which are essential to securing any deployment, lowering risk, and supplying constant visibility.
Top Security Concerns In Building A Data Warehouse
- Mapping out current info – You have to be able to categorize the information in your warehouse, comprehend where the private information is kept, and figure out who has access to it.
- Putting access controls in place – Start by ensuring adequate authentication, which typically entails integrating with your organization’s SSO control and may necessitate more stringent standards.
- Appropriate approval – Set the types of access that are needed and the people (who are typically given access through groups or roles) who need them.
- The access’s level of granularity – In many instances, fine-grained access control to the data warehouse is required to set various access levels depending on particular columns.
- Combating privilege abuse – Some data warehouse users may have more access than they need when projects change, personnel shift and setup issues arise.
- Monitoring and Auditing – Maintaining audits and keeping an eye on the queries and processes in the data warehouse is crucial.
- Behavioural Research – Risk factors like unethical behavior or insider threats should be considered before granting access to data.
- Ongoing Protection – Continuous security, as opposed to one-time projects, is required due to the extremely dynamic nature of data environments.
Applying Security Function Equalization
Consider this procedure your cloud security framework’s most crucial component and foundation. Utilizing least privilege capabilities, the goal—described in NIST Special Publication—can be achieved by separating security-related functions from non-security-related ones. Applying this idea to the cloud, your objective should strictly limit the cloud platform’s capabilities to their intended use. The only responsibilities for data lake positions in providing data lake engineering services should be maintaining and overseeing the platform.
Hardening Cloud Data Lake Platform
Start with a different cloud account to isolate and harden your cloud data lake platform. Limit the platform’s functionality to only those features necessary for administrators to operate and oversee the data lake platform. Using a separate account for your deployment is the most efficient technique for logical data separation on cloud platforms. You may quickly add a new account to your company if you utilize AWS’ Organization Management Service. The only additional expense associated with setting up new accounts is connecting this environment to your company using one of AWS’s network services.
By Assigning A Network Path, Ensuring Secure Flow
Designing the network path for the environment is crucial after hardening the cloud account. It serves as your initial line of protection and a crucial component of your security posture. There are many ways to secure the network perimeter of your cloud deployment; some will depend on your bandwidth needs and compliance standards, which may require using private connections. Other solutions include using VPN (virtual private network) services provided by the cloud and backhauling your traffic through a tunnel to your enterprise.
Traffic management and visibility are crucial if you intend to keep sensitive data in your cloud account and are not using a private link to the cloud. Utilize one of the numerous business firewalls available on the cloud platform markets.
Implementing Host-Based Security
Another crucial security layer in cloud deployments concerning data lake engineering services and data warehouses that are sometimes disregarded is host-based security.
Host-based security protects the host from attack and, in most situations, acts as the last line of defense, like how firewalls work to maintain network security. Depending on the service and function, the extent of safeguarding a host can change. Detailed instructions can be found here. The major processes include:
- Host intrusion management
- Advanced steps of File Integrity Monitoring (FIM)
- Key Process of Log Management
Enabling Identity Management and Provide Authentication
Identity is a crucial building block for auditing and offering reliable access control for cloud data lakes. Integrating your identity provider, such as Active Directory, with the cloud provider is the initial step in adopting cloud services. As an illustration, AWS offers detailed instructions on how to accomplish this using SAML 2.0. This might be sufficient for the identity of some infrastructure services. You may need to combine a patchwork of authentication systems such as SAML clients and providers, such as Auth0, OpenLDAP, and possibly Kerberos and Apache Knox if you decide to manage your third-party applications or construct data lakes with numerous services.
Providing Authorization and Standard Encryption
To protect sensitive data, authorization offers controls for data and resource access and column-level filtering. Cloud providers integrate robust access controls into their PaaS systems through resource-based IAM policies and RBAC, which may be customized to restrict access control using the principle of least privilege. The ultimate goal is to set access controls at the row and column levels centrally. In addition to offering data and workload engine access controls like Lake Formation and expanding the ability to transfer data between services and accounts, cloud providers like AWS have started to extend IAM. To ensure fine-grained permission, you might need to expand this strategy with additional open-source or third-party projects like Apache Ranger, depending on the number of services running in the cloud data lake.
Cluster and data security are fundamentally dependent on encryption. Best encryption methods may typically be found in instructions offered by cloud service providers. These details must be accurate, necessitating a thorough grasp of IAM, key rotation policies, and particular application setups.
Implementing Vulnerability Management Solutions
Regardless of your analytic stack and cloud provider, you should ensure that all the instances in your data lake architecture are patched with the most recent security updates. It would help if you implemented a regular patching strategy for your OS and software and routine security audits of your entire infrastructure.
Compliance Monitoring and Instant Response and Prevention of Data Loss
Compliance monitoring and incident response are the cornerstones of any security architecture for early detection, investigation, and response. Consider leveraging your on-premises security information and event management (SIEM) infrastructure for cloud monitoring if you already have it. Any market-leading SIEM solution can ingest and analyze all the significant cloud platform events.
Cloud data lakes should persist data on cloud object storage (like Amazon S3) with secure, affordable redundant storage, sustained throughput, and high availability to assure data integrity and availability. One of the additional capabilities is object versioning with retention lifecycles, which can enable the repair of unintentional deletion or object replacement.
The biggest security risk comes from somebody intercepting the data going into the lake or listening in on it to learn what the data is, where it might be coming from, and whether or not it contains any relationships. “This may seem like authoritarian overkill to the casual residents of the data lake, but it is the only way to secure and protect the data,”
Threats of concern include industrial espionage, such as obtaining process trade secrets, insider sabotage, and terrorism—bringing down a power grid. If you are looking for data mining services or data science services, then you can contact Hexaview Technologies. They have a team of highly skilled data scientists having deep expertise in data science and custom software development.