Finan­cial firms lever­ag­ing sen­si­tive data with Con­fi­den­tial Computing

As finan­cial firms face an end­less and unre­lent­ing array of dig­i­tal theft and fraud, they are increas­ing­ly lean­ing on a secu­ri­ty approach based on con­fi­den­tial com­put­ing. This con­cept has gained a lot of momen­tum very quick­ly across the industry.

How to lever­age sen­si­tive data with Con­fi­den­tial Computing

Con­fi­den­tial Com­put­ing focus­es on secur­ing data while it’s being processed, by using secure hard­ware-enabled enclaves. The data and code are then secure­ly stored inside the enclave, pro­tect­ing them from any unau­tho­rized access – either from sys­tem admin­is­tra­tors, hosts of the oper­at­ing sys­tem, oth­er ten­ants or the own­er of the infra­struc­ture itself. The enclaves are kept sep­a­rate from oth­er appli­ca­tions and ser­vices run­ning on the same sys­tem, mak­ing sure that the attack sur­face is kept to a min­i­mum. In today’s world, where busi­ness­es are think­ing “cloud-first”, this tech­nol­o­gy is of great impor­tance. Any work­loads that were pre­vi­ous­ly not con­sid­ered to be uploaded to the cloud, because of secu­ri­ty and com­pli­ance con­cerns, can now take advan­tage of these services. 

That’s appeal­ing for enter­pris­es that must man­age large amounts of sen­si­tive data that are fre­quent­ly in use and rapid­ly mov­ing among mul­ti­ple loca­tions: on-premis­es and in the cloud. Con­se­quent­ly, banks and oth­er finan­cial orga­ni­za­tions can great­ly ben­e­fit from Con­fi­den­tial Com­put­ing tech­nol­o­gy. 

Let’s talk examples 

Con­fi­den­tial machine learning 

Using Con­fi­den­tial Com­put­ing and com­bined with machine learn­ing, banks and finan­cial insti­tu­tions can now col­lab­o­rate to achieve bet­ter over­all results. For instance, two finan­cial insti­tu­tions can work on gen­er­at­ing cred­it qual­i­fi­ca­tions for cus­tomers by using CC tech­niques. They can com­bine their two sep­a­rate datasets into one with­in a secure enclave. They could share the cred­it his­to­ry of their cus­tomer to track and assess their cred­it score. Once the data is in this pri­va­cy box, no unau­tho­rized access is pos­si­ble. But AI appli­ca­tions and algo­rithms can still access this new com­bined dataset. Based on this, they track and assess the trans­ac­tion­al data and gen­er­ate new con­clu­sions. This will ben­e­fit both insti­tu­tions with improved out­comes. And this all while remain­ing own­ers of the pri­va­cy of their sen­si­tive data.

Fight­ing mon­ey laundering

Con­fi­den­tial com­put­ing secu­ri­ty is also effec­tive when it comes to efforts against mon­ey laundering. 

This approach would be based on an AI-based mon­ey frame­work, uti­liz­ing fed­er­at­ed learn­ing. It involves dif­fer­ent com­pa­nies that work col­lab­o­ra­tive­ly to obtain a shared pre­dic­tion mod­el. Fed­er­at­ed learn­ing allows the data to be kept in local envi­ron­ments, such as banks’ inter­nal sys­tems. They upload data to a cen­tral­ized node where AI algo­rithms pro­vide risk assess­ments, allow­ing banks and oth­er finan­cial insti­tu­tions to spot poten­tial risk can­di­dates. Fur­ther­more, banks could share and use each other’s trans­ac­tion data to build pre­dic­tive mod­els and cre­ate an anti-mon­ey laun­der­ing sys­tem. They can do all of this with­out expos­ing sen­si­tive data to their competitors.

“They can use con­fi­den­tial com­put­ing to make sure that the right pro­grams are oper­at­ing on the right data and that data can stay where it resides in the bank as opposed to being shared across indus­try bound­aries.”, said Michael Reed, Intel’s direc­tor of con­fi­den­tial com­put­ing in an inter­view.

Oth­er applications

But these are not the only appli­ca­tions for the tech­nol­o­gy in the finan­cial sector.

Oth­er exam­ples include:

• Detect fraud and dig­i­tal theft
• mar­ket-rate calculations
• access­ing finan­cial prod­ucts more quickly
• analyse loan applications

Con­clu­sion

With such appli­ca­tions, Con­fi­den­tial Com­put­ing has the poten­tial to rad­i­cal­ly change the way that the finan­cial sec­tor assess­es and shares sen­si­tive data about cus­tomers. With end to end encryp­tion and con­fi­den­tial­i­ty, banks can lever­age cus­tomers’ data to build and improve ser­vices, while help­ing them meet their com­pli­ance obligations.