Generative AI for Data Scrambling and Privacy and Protection in SAP
Does that scare you to imagine a world where your organization’s most critical data is not just scrambled and protected but fortified by Generative AI? While Generative AI poses lot of challenges to SAP regulatory, compliance and security aspects if it is not carefully used, I want to explore some of the ideas in the blog on how we can use the generative AI to meet these challenges. I am aware that this topic can be VERY controversial but those ideas are always experiments for continuous improvement:).
Please note that the ideas on this blog are my personal insights and is not an endorsement to use these ideas to implement data security on projects :)!
In the realm of enterprise data management, where the stakes are high, SAP systems stand as pillars, housing invaluable information that powers businesses worldwide, let’s review if Generative AI can be harnessed to safeguard the treasure trove of data residing in the heart of your SAP systems.
Generative AI Use cases for SAP Data Security
1. Anonymizing SAP Data
Within SAP’s virtual walls reside an array of sensitive information, from employee records to financial transactions. To enable SAP anonymisation, Generative AI can be used as a master of disguise by producing synthetic SAP data that mirrors reality’s statistical elegance while concealing the identities of individuals and the sensitivity of the data.
Example: In an SAP HR database, generative AI can create synthetic employee profiles with fabricated names, addresses, and Social Security numbers. These synthetic profiles maintain the same statistical distribution as real data but protect the privacy of individuals. This allows HR teams to test SAP updates and applications without exposing actual employee information.
2. Preserving SAP Data Utility
In the delicate art of data security, balance is key. We can use specific pre-trained models that ensure relationships data will stay intact while generating synthetic data for data that needs to be protected.
Example: Generative AI can be applied to SAP sales data. When generating synthetic sales records for testing purposes, the AI ensures that the relationships between customers, products, and sales representatives remain intact. This preserves the utility of the data for analytics and forecasting while safeguarding sensitive customer information.
3. Data Masking and Tokenization in SAP
To enable data protection, Generative AI can lend its expertise to SAP data masking and tokenization, shrouding or replacing personally identifiable information (PII) within SAP databases. Names, Social Security numbers, or credit card details can be rendered inaccessible, adding an impenetrable layer of security.
Example: Imagine an SAP system handling customer orders. Generative AI can tokenize customer names and credit card numbers, replacing them with unique identifiers. This way, only authorized personnel with the decryption key can access the original data, providing an extra layer of security for financial transactions.
4. Securing SAP Reports and Documents
SAP-generated reports and documents often serve as repositories of sensitive information. We can leverage generative AI to redact and substitute while preserving the context and relationships between data for doing meaningful insight analysis on data.
Example: SAP generates financial reports containing sensitive profit margins and cost breakdowns. Generative AI can automatically redact or replace these specific figures in reports shared with external auditors, ensuring the financial data’s security while allowing the auditors to assess the overall financial health of the organization.
5. Securing SAP Images and Attachments
In the visual realm of SAP, images and attachments conceal identifiable features. Generative AI can stand guard, blurring or obscuring these features, making them shareable or archival-worthy without compromising privacy.
Example: SAP often handles scanned documents like invoices. Generative AI can be used to automatically blur or pixelate sensitive information within these scanned documents, such as invoice numbers or vendor names. This ensures that the information remains private while preserving the integrity of the document.
6. Data Sharing in SAP
In the intricate tapestry of collaboration, Generative AI can unveil its magic by generating secure versions of SAP data. These versions are ideal for sharing with external partners or third-party consultants, safeguarding your data from unwanted exposure.
Example: A manufacturing company needs to share production data with a third-party consultant. Generative AI can generate synthetic production data that mimics the actual data’s patterns and trends. The consultant can perform analyses and provide recommendations without accessing the sensitive production processes.
7. SAP Honeypots for Cybersecurity
By crafting counterfeit SAP user profiles, login credentials, or transaction data, Generative AI creates diversions that confound potential cyber assailants. This strengthens your SAP environment’s ability to detect and respond to security threats with unparalleled precision.
Example: To detect cyber attackers within SAP systems, generative AI can create deceptive user profiles with enticing privileges. These “honeypot” profiles attract malicious actors, allowing security teams to closely monitor their activities and swiftly respond to potential threats.
8. IoT Security in SAP
The intersection of SAP and IoT presents unique challenges, can Generative AI can step up in generating decoy IoT data streams within SAP, veiling the true identities of your IoT devices and rendering them invisible to malevolent actors?
Example: In an SAP-integrated IoT environment, generative AI can generate decoy IoT data streams that mimic the behavior of real devices. These decoys act as a camouflage, making it challenging for hackers to distinguish between actual and fake IoT data, enhancing overall IoT security.
This is the tale of Generative AI’s indomitable role in safeguarding your SAP data, a saga that transcends the ordinary and paves the path to extraordinary data security if Generative AI is used carefully with right level of controls. As SAP systems continue to evolve as the lifeblood of modern enterprises, will the integration of Generative AI serve as a beacon guiding organizations towards a future where their data is impervious to threats, and user privacy is sacrosanct?
Tricky Riddle : Can we differentiate what data is secure and what data is insecure i.e what security is generated and what security is reality? What data is generated, what is immersive reality, what is actual fusion reality and what is real reality:)? What data am I referring to? Why did I give this riddle to the audience? :)!