Unlock­ing the Mys­tery of Con­fi­den­tial AI Models

Keep­ing Secrets Safe in the World of AI with Con­fi­den­tial Computing

As the use of Arti­fi­cial Intel­li­gence (AI) con­tin­ues to grow, so too does the impor­tance of keep­ing sen­si­tive data and con­fi­den­tial mod­els safe from pry­ing eyes. Enter con­fi­den­tial com­put­ing, a pow­er­ful tech­nol­o­gy that helps safe­guard data and AI mod­els from unau­tho­rized access. In this blog post, we’ll explore the world of con­fi­den­tial AI mod­els and dis­cuss how con­fi­den­tial com­put­ing can be lever­aged to keep your AI secrets safe. Whether you’re a data sci­en­tist or an AI enthu­si­ast, under­stand­ing the impor­tance of con­fi­den­tial AI mod­els is cru­cial in today’s data-dri­ven world. So, let’s dive in and dis­cov­er how you can keep your con­fi­den­tial AI mod­els secure with con­fi­den­tial computing.

Intro­duc­tion

Con­fi­den­tial Com­pute (CC) is a pow­er­ful new par­a­digm, embark­ing on the cloud com­put­ing space. In sim­ple terms, exe­cutes appli­ca­tions in a secure and trust­wor­thy, and encrypt­ed black box, so that the cloud provider can­not see any of the code and/or data being processed. The tru­ly inno­v­a­tive aspect of the tech­nol­o­gy is that not only the stor­age (“data in rest”) or trans­port (“data in tran­sit”), but for the first time also the pro­cess­ing of the data is always intrans­par­ent (“data in use”). This iso­lates the data pro­cess­ing from the oper­at­ing sys­tem and the appli­ca­tions run­ning on it. Dur­ing pro­cess­ing, nei­ther the (cloud) ser­vice provider, admin­is­tra­tor, nor a (com­pro­mis­ing) third par­ty has access to the data.

This type of tech­nol­o­gy is becom­ing increas­ing­ly impor­tant as more and more com­pa­nies look to lever­age the pow­er of AI and machine learn­ing to gain insights and make pow­er­ful pre­dic­tions from their data.

The ben­e­fits of CC for lever­ag­ing the pow­er of AI

One of the key ben­e­fits of con­fi­den­tial com­put­ing is that it allows orga­ni­za­tions to process sen­si­tive data with­out hav­ing to wor­ry about it being com­pro­mised or stolen. This is because the data is encrypt­ed and only decrypt­ed with­in the secure enclave, this “black box”, mak­ing it vir­tu­al­ly impos­si­ble for an out­side par­ty to access it. This is par­tic­u­lar­ly impor­tant for orga­ni­za­tions that deal with sen­si­tive infor­ma­tion such as per­son­al data, finan­cial trans­ac­tions, or con­fi­den­tial busi­ness information.

Anoth­er ben­e­fit of con­fi­den­tial com­put­ing is that it allows orga­ni­za­tions to share data with oth­er orga­ni­za­tions or third-par­ty ven­dors with­out hav­ing to wor­ry about the data being com­pro­mised. This is because the data is encrypt­ed and can only be decrypt­ed with­in the secure enclave, mak­ing it vir­tu­al­ly impos­si­ble for an out­side par­ty to access it. This is par­tic­u­lar­ly impor­tant for orga­ni­za­tions that need to share data with oth­er orga­ni­za­tions for research or col­lab­o­ra­tion purposes.

One of the most promis­ing areas for the appli­ca­tion of con­fi­den­tial com­put­ing is in the field of AI and machine learn­ing. With the increas­ing amount of data being gen­er­at­ed and the grow­ing com­plex­i­ty of AI mod­els, it is becom­ing increas­ing­ly impor­tant to ensure that sen­si­tive data is pro­tect­ed. Because by lever­ag­ing con­fi­den­tial Com­put­ing, orga­ni­za­tions across indus­tries can ensure com­plete secu­ri­ty for their data. Unlike tra­di­tion­al encryp­tion meth­ods that can leave data exposed in mem­o­ry, secure enclaves based on Intel® SGX tech­nol­o­gy offer a pro­tect­ed envi­ron­ment for exe­cu­tion, with a direct con­nec­tion to the hard­ware, effec­tive­ly block­ing unau­tho­rized access to con­fi­den­tial cus­tomer data.

How orga­ni­za­tions can lever­age CC for AI models

There are sev­er­al ways that orga­ni­za­tions can lever­age con­fi­den­tial com­put­ing to improve their AI and machine learn­ing models. 

One way to lever­age con­fi­den­tial com­put­ing is through the use of “fed­er­at­ed learn­ing” algo­rithms. These algo­rithms allow orga­ni­za­tions to train AI mod­els on data that is dis­trib­uted across mul­ti­ple devices, with­out hav­ing to move the data to a cen­tral loca­tion. This allows orga­ni­za­tions to train AI mod­els on sen­si­tive data with­out hav­ing to wor­ry about the data being com­pro­mised. This could be par­tic­u­lar­ly inter­est­ing with­in the fin­tech indus­try, for instance 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.

Fur­ther­more, orga­ni­za­tions could lever­age con­fi­den­tial com­put­ing to improve their AI and machine learn­ing mod­els by com­bin­ing data sets from dif­fer­ent insti­tu­tions and orga­ni­za­tions, with­out exchang­ing the actu­al sen­si­tive data. While using secure enclaves before send­ing the data sets, data own­ers can ensure that any AI mod­el based on these data sets is not expos­ing the actu­al users’ pri­vate infor­ma­tion, but rather only the encrypt­ed data sent.

For exam­ple, mul­ti­ple hos­pi­tals can com­bine their data to train AI for detect­ing dis­eases, say, giv­en pic­tures from CT scans. Exchang­ing data for research pur­pos­es would not come at the detri­ment of data pri­va­cy, though. Work­ing with­in enclaive’s Con­fi­den­tial Con­tain­ers ensures that patients’ data remains con­fi­den­tial dur­ing each step of the process. This way, the patien­t’s pri­va­cy is pro­tect­ed and hos­pi­tals or oth­er data own­ers (i.e. research insti­tu­tions) remain in con­trol of their valu­able data.

Wrap-up

In con­clu­sion, con­fi­den­tial com­put­ing may be a rel­a­tive­ly new tech­nol­o­gy, but it is gain­ing a lot of attrac­tion across indus­tries. It’s also becom­ing increas­ing­ly impor­tant as more and more orga­ni­za­tions look to lever­age the pow­er of AI and machine learn­ing to gain pow­er­ful insights and make pre­dic­tions from their data. By enclos­ing sen­si­tive data and com­pu­ta­tions in a secure enclave, con­fi­den­tial com­put­ing pro­vides a way to pro­tect sen­si­tive data and com­pu­ta­tions from being com­pro­mised. Addi­tion­al­ly, the tech­nol­o­gy enables orga­ni­za­tions to share data with oth­er orga­ni­za­tions or third-par­ty ven­dors with­out hav­ing to wor­ry about the data being com­pro­mised. With the increas­ing amount of data being gen­er­at­ed and the grow­ing com­plex­i­ty of AI mod­els, con­fi­den­tial com­put­ing pro­vides a way to ensure that sen­si­tive data is pro­tect­ed and to improve AI and machine learn­ing models.