ID Consent: applying the IDaaS Maturity Framework to design and deploy interactive BYOID (Bring-Your-Own-ID) with Use Case


Current approaches to IDaaS on one hand enforce trust of consumer data using legal compliance, risk and impact assessment and the other hand require technical implementation of access controls to personal data held by an enterprise. Balancing trust has to be done across all layers, verifying person’s identities, showing the individual and the service is real, creating short term relationships and verifying and maintaining all long the Cloud service the user mapping between the enterprise and the cloud user account in a mesh federation. This makes sense only if enterprises design “on-premise” with MaaS their own flexible ID data model and can verify ID maturity and consistency before moving, and along, the ID service in the Cloud. Based on MaaS, the BYOID concept is a possible solution to ID models for consent policy design, management and deployment. The BYOID model is a means to expressing, tracing and updating consumer’s personal data policy requirements; however enterprise users’ privacy preferences are provided as well. The IDaaS Maturity Framework (IMF) defines and directs the BYOID practice. MaaS guide properties and personal preferences from the consent metamodel design to the ID deployment. Both ensure that ecosystem compliance is achieved and ID in the Cloud meets trustworthy relationships.

IMF supports flexible BYOID design and deployment

IDaaS is authentication and authorization infrastructure that is built, hosted and managed through different models by third-party service providers, resident in ID ecosystem frameworks. IDaaS for the enterprise is typically purchased as a subscription-based managed service.  One or more cloud service providers, depending upon the IDaaS model the enterprise deploys, may host applications and provide subscribers with role-based web access to specific applications or even entire virtualized infrastructure. IDaaS makes enterprises responsible in evaluating privacy risks and grade of confidence when moving the ID to the cloud. Accordingly, before externalizing the corporate IdM, consider the different IDaaS models are supported depending upon the maturity levels of:

– IdM/IAM system, in terms of implementation, maintenance and IdM/IAM governance capacity. ID, by its nature is de-centralized and then the maturity rank should consider the whole IdM/IAM system including data protection, data manageability, data security and organization ID awareness at all levels;

IMF BYOID Fluid LifecycleFig. 1 – An example of enterprise BYOID consent model lifecycle to IDaaS deployment and reconciliation

– SOA system, to really understand policies by applied processes’ de-coupling (privileges by user role, accreditations, de-accreditations …) and procedures dynamically acting into the organization;

– ID ecosystem reliability and adherence to the frameworks’ security criteria that measure service provider(s) compliance.

However, the levels of maturity gauged along the organization enables the enterprise to design its own ID as a consequence of the appropriate IDaaS model. The enterprise is able to bring in the ID ecosystem a configurable IDaaS model based on MaaS design to satisfy enterprise business rules. Business rules have impact on enterprise identity requirements and they balance and reconcile consumer identities needs. This “fluid” multiple-way enterprises-consumers solution, or BYOID, creates a high assurance level of ID ecosystem participants’ identities that could be used for enterprise access by respecting privacy and security requirements: IDaaS models contain BYOID properties and define “on-premise” BYOID maturity and consistency.

A new concept of ID consent: the BYOID fluid model

When registering to an Identity Platform, users would like represent themselves according to their behaviour having the option to approve selective or discretionary sharing of their private information and looking for the ability to obfuscate, mask or mesh some parts of personal data. So, ID platform and user are creating interactively a bond of trust as a part of the whole ID service. This is possible only if the consent of the individuals, the data protection conditions for processing their personal data and consent policies might be modelled “on-premise” by the enterprise IdM.

Looking at the IMF, the ID metamodel might sprout in the IdM/IAM maturity appraisal stage, according to the properties and requirements the enterprise needs to protect personal data and sensible information. The question now is the following: if the ID metamodel is designed in the company IdM, could the consent model be considered proprietary? The metamodel gathers the properties corresponding to the real enterprise requirements but it will be tested and appraised firstly in IdM/IAM system and then in the SOA maturity system. At that point features like interoperability, expression of functionality and user’s behaviour will be explicit aspects of the BYOID data model such as the following:

1)    Trust properties;
2)    Verification;
3)    Scalability and performance;
4)    Security;
5)    Privacy;
6)    Credential Types;
7)    Usability;
8)    Attributes;
9)    User Centricity/User Control.

The above properties are matter for the ID ecosystem public consent data model structure (basic/incoming tables of the BYOID metamodel). In the beginning, those metadata are properties of the company: the company’s BYOID metamodel. Once the BYOID metamodel has been defined, tested and approved as BYOID company data model, it will be released to the ID ecosystem as an IDaaS model subscription. Despite of different approach, each enterprise may then adopt and release his BYOID. Before deploying BYOID services in the Cloud, the BYOID model should be compared with other BYOID models already running into the ID ecosystem frameworks. To be accepted, BYOIDs have to meet a set of common requirements enforced by the consent public ID ecosystem framework authority: the more adaptive is the public consent model (continuously and rigorously improved), the more flexible, secure and reliable are the BYOIDs shared. It makes interactive, fluid and safe BYOIDs deployed through IDaaS. Still, this enables user’s behaviour can be captured both at high level (enterprise-ecosystem reconciliation) and at low level (personal-enterprise-ecosystem reconciliation). Therefore BYOID can be reconciled, renormalized and constantly trusted at all levels. Since BYOID metamodel contains the enterprise identity requirements, it might include and integrate the ID ecosystem identity properties and, if approved by the user (obligation to maintain the personal data securely), his personal properties. This aspect is very important: in fact, there’s significant risk for a company when both customer/user relationships and company data are stored on personal devices. Using BYOID deployed as an IDaaS subscription, company information is centralized based upon “on-premise” consent metamodels: this means that company information stored on personal devices is minimized and always centrally controlled.

BYOID Model Recon

Fig. 2 – Fluid BYOID update and reconciliation: IDaaS User Experience vs. BYOID IDaaS subscription

User’s personal properties might reside on the same company (central) metamodel/consent model or not depending upon user approval and, always possible, withdrawal (i.e. personal data should comply with data protection legislation and, where necessary, the approval of the individual must be obtained). In the figure 2 here is an example. In 1 the User tries a new behaviour (statistically relevant or as a recommender system outcome); in 2 the IDaaS user experience has to be changed and updated. Above we show 3 data models but in the MaaS representation they consist of a unique model containing the BYOID IDaaS subscription (master) that includes 2 sub-models: the company consent model and the user personal model. In 3, the consent model is modified to keep compliance with the company business rules/conduct mapped to the BYOID IDaaS subscription. In 4, finally the update is executed and the User might find his conduct as a new function. However, take note in the figure 2 a relational model-like formalism is applied. This is just a simplification. In effect, we are in a multi-level relational data model that can be represented with NoSQL, Vector or Graph DB else, depending upon the data analytics domain.

USE CASE: the fluid BYOID approach


IDaaS models to move ID to the Cloud enable organizations to externalize identities data more knowingly and securely. Employees and customers behaviour changed: they continuously have business contacts, calls and meetings with personal devices. Since an increasing quantity of employees uses their mobile devices everywhere, identities can be resident and so associated to applications running on different framework in a multi-topology cloud configuration. What should be then the best IDaaS model satisfying this new employees/customers conduct? Could be managed all users, across multiple locations, while securing company data? Because of each identity may be managed by different identity management services, authentication and validation of identities by the cloud infrastructure could not be sufficient. Companies have to verify and control “on-premise” their ID maturity. BYOID based upon IDaaS models allows to identifying and securing identity properties. Further IDaaS models assist ID integrity control over shared topologies with a variety of ID ecosystem frameworks. IMF plays a crucial role in identifying the most appropriate IDaaS model before deploying the BYOID to the Cloud. Then the BYOID is an IDaaS model and can be designed “on-premise” and controlled along deployment and subscription.

Properties and Directions

This use case is concerned with enterprises deploying their BYOID in the Cloud using IDaaS models and IMF. There is a need for evaluating “on-premise” organization IdM/IAM and SOA maturity before moving the ID to the Cloud. Evaluating the organization maturity levels involves three steps:

  1. IdM/IAM maturity: measure the IdM/IAM maturity level;
  2. SOA maturity: measure SOA maturity level – policies (privileges by user role, accreditations, de-accreditations …) and processes dynamically acting;
  3. Identity Ecosystem reliability/maturity: measure the ecosystem maturity/reliability, and above all, the secure service continuity because in hybrid topologies identities may be owned by different cloud providers resident in multi-topologies.

Objectives are the following:

  • Enable organization to identify and set the best BYOID through IDaaS model based upon internals levels of IdM/IAM and SOA maturity compared to the ID ecosystem framework’s baseline adherence. This sets maturity in classifying the ID ecosystem framework and in evaluating the reliability the ID ecosystem may provide;
  • Deploy the proper BYOID model applying the correct subscription and adherence with respect to the IDaaS ecosystem;
  • Periodically measure the organization’s IdM/IAM and SOA maturity levels and verify the ID ecosystem reliability/maturity so to update, and eventually scale, the BYOID deployed.

However, accordingly with the objectives, the value of the ID ecosystem level of reliability/maturity is the outcome the company is expecting to:
–          Keep BYOID secure and controlled and supervise the IDaaS service subscription;
–          Contribute to the ecosystem as participant and/or as authority;
–          Be a participant/counterpart in setting and approving attributes providers, policies and relying party’s decisions and IDaaS ecosystem adherence;
–          Contribute to the IDaaS Trustmark definition and to the periodical appraisal and updating.


Table 1 – BYOID Use Case properties and directions

Process Flow along the IMF

Accordingly to this Use Case, the IMF process flow encompasses three steps:

Part 1: Appraise IdM/IAM Maturity Level – To cover definition, maintenance and upgrade of the organization IdM/IAM level of maturity. The IdM/IAM maturity value has to be periodically monitored and controlled to keep coherence with the IDaaS model deployed:

Use Case 1.1

Figure 3 – BYOID: IDM/IAM Maturity Level Appraisal

The Identity and Access Manager verifies the Maturity level of the IdM/IAM system:

  • The IdM Manager controls and regulates the accesses to information assets by providing policy controls of who can use a specific system based on an individual’s role and the current role’s permissions and restrictions. This ensures that access privileges are granted according to one interpretation of policy and all users and services are properly authenticated, authorized and audited;
  • The BYOID Manager reconciles BYOID metadata and update the BYOID metamodel.

The IAM Manager controls if users’ identities can be extended beyond corporate employees to include vendors, customers, machines, generic administrator accounts and electronic access badges, all ruled by policy controls.

Part 2: Appraise the SOA Maturity Level – To cover definition, maintenance and upgrade of the organization SOA maturity level. The SOA maturity level has to be periodically monitored and controlled to keep coherence with the BYOID released:

Use Case 1.2

Figure 4 – BYOID: SOA maturity level appraisal

The SOA Manager verifies the Maturity level of the SOA system through the SOA interoperability and defines the organization maturity in sharing services among departments:

  • The SOA Manager verifies that the map of communications between services is drawn starting from IdM/IAM system and achieved maturity
  • The SOA Manager controls and reports about the following crucial aspects:
  • SOA reference architecture achievements and evolution;
  • education to broaden SOA culture through the organization;
  • methods and guidelines that organization adopts to apply SOA;
  • policy for SOA appliance and governance.
  • The BYOID Practice Manager tests and executes BYOID consent model reconciliation based on metamodel reconciliation and update. If necessary, BYOID Manager renormalizes the consent model by roundtrip with the BYOID metadata at IdM/IAM maturity level.

Part 3: Appraise the ID Ecosystem Reliability/Maturity – To establish the maturity/ reliability of the ID Ecosystem Posture. The comparative maturity of BYOID (Company vs. ID Ecosystem participants vs. user preferences) has to be continually monitored: points of discontinuity, unmatched policies, and untrusted relationships have to be time by time acknowledged. This helps to better qualifying frameworks accountability, federation assets, and participants’ reliability and level of contribution:

Use Case 1.3

Figure 5 – BYOID: ID Ecosystem Maturity/Reliability Appraisal

The Service Manager verifies the Maturity/ Reliability level of the ID Ecosystem framework:

  • The Service Manager controls that contribution to the ecosystem by privacy aspects, security components and accountability mechanism settings are congruent
  • The Service Manager controls that common guidelines keep coherence with the company policies and standards strategy. Since more than a framework exists inside the ecosystem, rules to ensure that accreditation authorities validate participants’ adherence to the ecosystem requirements are to be verified and updated
  • The Service Manager controls adherence to the ID ecosystem of the IDaaS deployed to verify reliability and service continuity;
  • The Service Manager verifies that accreditation authority to ensure participants and frameworks are adherent to the identity ecosystem interoperability standards accepted
  • The Service Manager controls that the ID ecosystem contains all trusted frameworks that satisfy the baseline standards established and they are compliant with the company maturity level
  • The BYOID Practice Manager verifies the framework ecosystem common levels of adherence (baseline) and test and compare BYOID reliability properties;
  • The ID Ecosystem Management Service verifies BYOID adherence and security with respect the IDaaS subscription.

The ID Ecosystem Management service provides a combination of criteria to determine the service providers’ compliance among frameworks and ID ecosystem topologies: the combination defines policies, rules and, eventually, a Trustmark. It gives confidence to participants in deciding who to trust in terms of BYOID framework adherence and among all ID providers.


Managing digital identities across ID ecosystems frameworks is crucial to improve efficiency of business collaborations. Using everywhere personal devices is becoming a preferred conduct but before sharing the ID among cloud domains, all involved parties need to be trusted. Still, to meet the demanding needs of security, big data analytics and business intelligence, users and consumers need a more efficient and flexible paradigms. In this paper, we identify how BYOID fluid model satisfies on one hand company security and user data protection and, on the other hand, rapid updating and reconciliation to the user conduct. IMF provides the necessary platform for collaboration in ID ecosystem topologies. We introduce also a USE CASE to point out how BYOID built across ID company consent model and ID ecosystem trusted access model, can be a foundation to gauge and govern BYOID strategies. Further, the paper can be used to compare different BYOID IDaaS subscription to establish what maturity levels the company might support compared with all business partners running existing IDaaS maturity models and to ensure ID in the Cloud meets trustworthy relationships.


I have to sincerely thank Susan Morrow for the precious feedback on contents and Anil Saldhana for the useful comments on the IDaaS Maturity Framework.


N. Piscopo – IDaaS. Verifying the ID ecosystem operational posture
N. Piscopo – A high-level IDaaS metric: if and when moving ID in the Cloud
N. Piscopo – MaaS implements Small Data and enables Personal Clouds
N. Piscopo – Best Practices for Moving to the Cloud using Data Models in the DaaS Life Cycle
N. Piscopo – MaaS (Model as a Service) is the emerging solution to design, map, integrate and publish Open Data
N. Piscopo – MaaS applied to Healthcare – Use Case Practice
N. Piscopo – Applying MaaS to DaaS (Database as a Service) Contracts. An introduction to the Practice
N. Piscopo – Enabling MaaS Open Data Agile Design and Deployment with CA ERwin®
N. Piscopo – ERwin® in the Cloud: How Data Modeling Supports Database as a Service (DaaS) Implementations
N. Piscopo – CA ERwin® Data Modeler’s Role in the Relational Cloud
N. Piscopo – Using CA ERwin® Data Modeler and Microsoft SQL Azure to Move Data to the Cloud within the DaaS Life Cycle
N. Piscopo – Page 16 in Transform2, MaaS and UMA implementation

Disclaimer – This document is provided AS-IS for your informational purposes only. In no event the contains of “ID Consent: applying the IDaaS Maturity Framework to design and deploy interactive BYOID (Bring-Your-Own-ID) with Use Case” will be liable to any party for direct, indirect, special, incidental, economical (including lost business profits, business interruption, loss or damage of data, and the like) or consequential damages, without limitations, arising out of the use or inability to use this documentation, regardless of the form of action, whether in contract, tort (including negligence), breach of warranty, or otherwise, even if an advise of the possibility of such damages there exists. Specifically, it is disclaimed any warranties, including, but not limited to, the express or implied warranties of merchantability, fitness for a particular purpose and non-infringement, regarding this document use or performance. All trademarks, trade names, service marks and logos referenced herein belong to their respective companies/offices.

A high-level IDaaS metric: if and when moving ID in the Cloud


Building metrics to decide how and whether moving to IDaaS means considering what variables and strategy have to be taken into account when organizations subscribe identity as a service contracts. Before moving any IdM to the Cloud, organization should balance costs and risks. Accordingly, metrics adopted should be enough flexible to be applied from both a company that is developing an IdM system and a company that already has a IAM in operation but is considering to move the ID to the Cloud. The metric introduced below is included into a coming IDaaS Best Practices helping companies to understand, evaluate and then decide if and how moving ID to the Cloud.

IDaaS: Measure Maturity

IDaaS metric definition starts from on-premise IdM/IAM acquisition and implementation costs. Take into consideration the following parameters:
1)  COSTS – IdM/IAM costs are mainly based upon Infrastructure, Personnel, Administration (Access, Help desk, Education/Courses, ..), Attestation and Compliance (including personnel certification and upgrading), Business Agility expenditures;
2) RISKS – Risks are based upon expenditures to cover by order:
2.1 Implementation risks (the risk that a proposed investment in technology may diverge from the original or expected requirements);
2.2  Impact risks (the risk that the business or technology needs of the organization may not be met by the investment in the IAM solution, resulting in lower overall total benefits);
2.3 System protection (perimeter defence, audit and surveillance).

The risk/confidence the company is dealing with depends mainly upon the combination of:
– IAM maturity, in terms of implementation, maintenance and evolution capacity;
– SOA maturity, to really understand policies by applied processes (privileges by user role, accreditations, de-accreditations, …) and dynamically acting into the organization;
– Adherence to the criteria that measure service provider(s) compliance with the identity ecosystem framework.

IDaaS Maturity2

Figure 1 – IDaaS Maturity Framework to IDaaS Best Practices

Accordingly, the metric should be based upon the organization maturity grade. The gauge proposed is made the simplest possible, designed to be flexible: if necessary, this metric can be enriched and applied to more complex systems (more parameters by maturity levels, more maturity levels according to the company’s policy). The metric measures what is the confidence/risk when organizations moves to IDaaS by adopting the following models:

1)    ID On-premise – ID is outsourced but infrastructure is kept inside the company. In this case ID personnel manage tools and infrastructure but expertise is coming from the outsourcer;
2)    ID Provider Hosted – A private Cloud for IDaaS is managed. Personnel managing the private Cloud (tools) are shared with the service Provider. In this case administration, tools and infrastructure are in the private Cloud and ID management is shared;

Flux IDaaS Schema2

Figure 2 – IDaaS properties and possible path to the Cloud

3)    ID Hybrid – IDaaS is in the Cloud although sensitive information is yet managed internally. ID Hybrid means subscribing private, community and/or public Cloud services. Tools and infrastructure are shared through the Cloud. ID administration is managed in the Cloud.
4)    ID in the Cloud – The ID is in the Cloud. Only personnel managing contract and service conditions (all aspects: policy, framework, SLA …) are kept internally.

These aspects are important on one hand considering what risk (and countermeasures) may be taken when moving the ID to the Cloud and on the other hand which takings could be expected in terms of cost savings. Companies have to balance the real business value of the risks based upon on-premise ID maturity and the eventual cost reduction, model by model. In the following picture, an example shows how 3 companies having 3 different levels of maturity for IdM, SOA and Ecosystem adherence, meet 3 scenarios in term of Cost/Saving and Confidence/Risk when decide to move to IDaaS.

Cost-Risk graph2

Figure 3 – IDaaS: 3 cases of companies having different level of maturity and risk

Company A – Company A manages advanced projects to implement and maintain high levels of maturity for IdM and SOA. Still, attention is paid to the Cloud identity ecosystem: the Company applies specific criteria to assess services provisioning in the Cloud. By applying IDaaS Best Practices based on Maturity levels, Company A might moderate the risks if decides to move ID in the Cloud. Criteria to adopt Cloud services are enough stable to manage on-demand and full provisioning IDaaS. Cost saving is another aspect should be taken into consideration. By externalizing IDaaS, the expected savings might be impressive (about 70% of CapEx invested) and, in this case, moving to the Cloud can be balanced with a path that further moderates the risk.

Company B – Company B has an intermediate maturity and work in progress projects through the IdM and SOA implementation. The ecosystem interface knowledge also is increasing although it is not yet disciplined. Confidence to move ID to the Cloud is low with respect the Company A and the risk is growing with the above IDaaS models. Considering the CapEx to implement internal IAM and BPM procedures, IDaaS cost saving is lower (about 30% of CapEx invested) then Company A. Company B should mitigate the risk by moving to the appropriate IDaaS model. The right path to subscribe IDaaS should be starting from the most proper IDaaS model to progressively increase levels of maturity.

Company C – Company C has a different challenge to get, with respect Company A and B. Company C is not organized to set defined levels of maturity for IdM and SOA. Still, there is not enough interest or experience to classify proper requirements and accountability mechanisms typical of an identity Cloud ecosystem structure. Identity and SOA cultures exist but they are jeopardized. In this case without CapEx to cover, it seems highly attractive saving soon by moving to IDaaS. However, cost saving only is not the best way, generally speaking, to move to the Cloud, neither to subscribe IDaaS contracts. The risk to move ID in the Cloud is really high. The Company C should ask for:

–      how IDs are provisioned, authenticated and managed (IdM, IAM);
–      who retains control over ID policies and assets (SOA);
–      how are stringent peer to peer security standards (ID ecosystem);
–      how and where are employed data encryption and tokenization (ID ecosystem);
–      how and where are employed federated identity policies (for example: check if they are regularly backed by strong and protected authentication practices) (SOA);
–      what about availability, identity data protection and trust on third parties (ID ecosystem);
–      how is employed transparency into cloud operations to ensure multi-tenancy and data isolation (IdM and ID ecosystem).

Could Company C provide the above answers before movingthe ID to the Cloud? This essential information should be an asset for any company that decide to migrate to the Cloud. Prerequisites above are only a part of the full requirements subscribers should assert before acquiring Cloud ID services. No Company can improvise to move to IDaaS: consequently, possible choices for Company C may be the following:
1) starting from the low risk ID on-premise model;
2) moving in any case ID to the Cloud being aware of the risk by trying to balance IDaaS cost saving (OpEx) benefit and Cloud environments introducing transient chains of custody for sensitive enterprise data and applications.

Defining the Metric
The metric that should best describe the above scenarios is based on the products of exponential functions depending upon parameters setting the organization maturity levels. In practice, the general mathematical relationship is the following:

Risk Formula2

Here is the meaning of variables and indexes:
R is the Risk/Confidence value defining the range maturity forward the IDaaS model above described;
Pcis the percentage of completion of each maturity range;
V is the variable corresponding to the magnitudes chosen to measure the maturity of the specified range. To calculate the level of IDM, SOA and Ecosystem maturity, 2 variables have been chosen: the project cost (Cm is the current cost and CM the estimated budget cost) and the project time completion (Tm is the current project time and TM the estimated project completion time);
N is the number of maturity ranges considered (IdM, SOA, Ecosystem …).
Constraints: the exponential function is a pragmatic risk estimation based upon the concept of density of probability. To compute the risk/confidence there is no average technique included: the max of the series of the calculated risks has been preferred with respect to the statistical averages models. Looking at the above metric, it requires the following constraint: 3 maturity ranges should be at least considered to estimate the best IDaaS model. They are: IdM, SOA and Ecosystem Framework. Further, the above metric is extensible and it is enough flexible to consider more ranges of maturity and, inside each one, more variables to be added to projects costs and times. Finally, R (risk/confidence) is computed as the max value among maturity series’ risks. In practice, consider the following test rates:

IdM Maturity: Percent of completion 30%, Cm = 25.000,00 $, CM = 75.000,00 $, Tm = 6 months and TM = 24 months
SOA Maturity: Percent of completion 40%, Cm = 55.000,00 $, CM = 90.000,00 $, Tm = 8 months and TM = 24 months
Ecosystem Framework Maturity: Percent of completion 15%, Cm = 10.000,00 $, CM = 30.000,00 $, Tm = 2 months and TM = 6 months

Risk/confidence outcomes based upon the above values are the following and the max value is:

Risk Formula Outcome2

Could the company accept the risk of 98% in moving to the Cloud with the ID system? What is the main pain looking at the maturity ranges and the risk rates? What is the appropriate IDaaS model could moderate the risk and reduce the costs? The solution in the figure below might be a measured solution to get confidence and awareness before subscribing an IDaaS contract.

Ballot Cost-Risk graph2

Figure 4 – Snapshot based upon the above maturity rates and risk/confidence values


Companies could apply a systematic approach by adopting the gauge above exploited. The metric can help in deciding whether balancing risks and OpEx advantages is appropriate in subscribing an IDaaS contract forward security and business benefits.  Looking at the cost saving for Company C, the above cutbacks could be modest (about 20% or less with respect the actual CapEx) although the ROI would be faster. It depends upon the IDaaS strategy the Company decides to implement.


[1] N. Piscopo – Applying MaaS to DaaS (Database as a Service) Contracts. An introduction to the Practice
[2] N. Piscopo – Best Practices for Moving to the Cloud using Data Models in the DaaS Life Cycle
[3] N. McEvoy – IDaaS Identity-as-a-Service best practices
[4] E. Baize et al. – Identity & Data Protection in the Cloud
[5] F. Villavicencio – Advantages of a Hybrid Co-Sourced IDaaS Model
[6] Identity in the Cloud Outsourcing Profile Version 1.0 – OASIS Committee Note Draft 01 /
Public Review Draft 01
[7] N. Piscopo, N. McEvoyIDaaS – Introduction to the Identity in the Cloud
[8] WG-CloudIDSec IDaaS (Identity as a Service)

Disclaimer – This document is provided AS-IS for your informational purposes only. In no event the contains of “A high-level IDaaS metric: if and when moving ID in the Cloud” will be liable to any party for direct, indirect, special, incidental, economical (including lost business profits, business interruption, loss or damage of data, and the like) or consequential damages, without limitations, arising out of the use or inability to use this documentation, regardless of the form of action, whether in contract, tort (including negligence), breach of warranty, or otherwise, even if an advise of the possibility of such damages there exists. Specifically, it is disclaimed any warranties, including, but not limited to, the express or implied warranties of merchantability, fitness for a particular purpose and non-infringement, regarding this document use or performance. All trademarks, trade names, service marks and logos referenced herein belong to their respective companies/offices.

“Policy as a Service” – Critical for Cloud Deployments!

ulrichThe financial ROI of Cloud security and compliance is judged by decision makers in end-user organizations by the same measures as is done for Cloud computing in general, i.e. by how much it cuts up-front capital expenditure and in-house manual maintenance cost.

However, manually translating security policy into technical implementation is difficult, expensive, and error-prone (esp. for the application layer). In order to reduce security related manual maintenance cost at the end-user organization, security tools need to become more automated.

With the emergence of Cloud PaaS, it is therefore logical to move all or parts of the model-driven security architecture into the Cloud to protect and audit Cloud applications and mashups with maximal automation. In particular, policies are provided as a Cloud service to application development and deployment tools (i.e. “Policy as a Service”), and policy automation is embedded into Cloud application deployment and runtime platforms (i.e. automated policy generation/update, enforcement, monitoring).

Different Cloud deployment scenarios are possible, which differ from local non-Cloud deployments where model-driven security is conventionally installed within or alongside a locally installed development tool (e.g. Eclipse). Policy as a Service (see ObjectSecurity OpenPMF) involves five parts:

1. Policy Configuration from the Cloud: Policy configurations are provided as subscription-based Cloud service to application development tools. Offering specification, maintenance, and update of policy models as a Cloud service to application developers and security experts has significant benefits:

Most importantly, instead of having to specify (or buy and install) and maintain the policy models used for model-driven security on an on-going basis, application developers and security specialists can now simply subscribe to the kinds of policy feeds they require without the need to know the details of the models.

The Policy as a Service provider (typically different from the Cloud provider) takes care of policy modeling, maintenance, and update. Other benefits are that the user organization does not need to be a security and compliance expert because the up-to-date policy models will be provided as a feed to them on an on-going basis, that the upfront cost hurdle is minimized thanks to the subscription model, and that there is no need by the end user organization to continually monitor regulations and best practices for changes.

2. Automatic Technical Policy Generation in the Cloud: The automatic policy generation feature of MDS is integrated into the development, deployment, and mashup tools (to get access to functional application information).

It consumes the policy feed described in the previous section. Platform as a Service (PaaS) sometimes includes both Cloud hosted development and mashup tools and a Cloud hosted runtime application platform. In this case, automatic technical policy generation using model-driven security (MDS) can also be moved into the Cloud, so that technical security policies can be automatically be generated for the applications during the Cloud hosted development, deployment and/or mashup process.

This is in particular the case for mashup tools, because those tools are more likely to be Cloud hosted, are often graphical and/or model-driven, and are concerned with interactions and information flows between Cloud services. If the development tools are not hosted on the PaaS Cloud, then the MDS technical policy auto-generation feature needs to be integrated into the local development tools.

3. Automatic Security Policy Enforcement in the Cloud: Policy enforcement should naturally be integrated into the PaaS application platform so that the generated technical policies are automatically enforced whenever Cloud services are accessed.

As described in the previous section, policies are either generated within Cloud using hosted MDS and PaaS development tools, or are uploaded from local MDS and development tools. How policy enforcement points are built into the PaaS application platform depends on whether the PaaS application platform (1) allows the installation of a policy enforcement point (e.g. various open source PaaS platforms, e.g. see case studies below), (2) supports a standards based policy enforcement point (e.g. OASIS XACML), or (3) supports a proprietary policy enforcement point.

4. Automatic Policy Monitoring into the Cloud: Policy enforcement points typically raise security related runtime alerts, especially about incidents related to invocations that have been blocked. The collection, analysis and visual representation of those alerts can also be moved into the Cloud.

This has numerous benefits: Incidents can be centrally analyzed for multiple Cloud services together with other information (e.g. network intrusion detection). Also, an integrated visual representation of the security posture across multiple Cloud services can be provided, integrated incident information can be stored for auditing purposes, and compliance related decision support tools can be offered as a Cloud service.

5. Automatic Updating: The described model-driven approach enables automatic updates of technical security policy enforcement and auditing whenever applications and especially their interactions, change. The same automation is possible when security policy requirements change.

Publications about this can be found in the ISSA Journal October 2010 and on IBM developerWorks. Contact me if you would like to know more information about Policy as a Service.

It is also important to note that model-driven security (MDS) does not necessarily rely on model-driven development to work – even though it relies on application, system, and interaction models (so-called “functional models”) to achieve significant security policy automation.

The traditional MDS approach is that these functional models ideally come from manually defined application models authored during model-driven development (e.g. UML, BPMN). But this is not necessary. We have designed an additional solution for our OpenPMF where the functional models are in fact obtained from an IT asset management tool that is part of our partner’s (Promia, Inc.) intrusion detection/prevention product Raven. This works well, and enables the use of model-driven security in environments which do not support model-driven development or where model-driven development is not desired.

While this may not sound like a big deal, it is in fact a big deal, because it increases the widespread applicability of model-driven security dramatically, and makes adoption a lot easier.

(note: this was cross-posted from by Dr. Ulrich Lang, CEO, ObjectSecurity