DAM in GDPR context

The rumor about GDPR provides to situation that customers receive messages that all existing security solution have “something” for that :). It is good sale strategy but definitely painful tactics for Security Officers with limited budget and hard nut to consume before 25 May, 2018.

Here I would like to review GDPR requirements (AS-IS, because still the European Data Protection Council did not provide certification guideline) from DAM perspective and review the most popular questions tied with DAM in the GDPR context.

Where DAM cover GDPR requirements?

  • Article 5.1(f) – Data protection principles assumes protection against unauthorized or unlawful processing and against accidental loss, destruction or damage, using appropriate technical or organizational measures
    DAM is dedicated solution to monitor SQL stream – granular policies can narrow access only to accepted vectors, behavioral analysis identifies anomalies, prevention rules block suspicious activity,  SQL analysis dynamically masks data and even stops execution of dangerous commands.
    Administrative fines and possible civil actions should change the PI administrators approach and consider the manual organizational measures as not sufficient.
  • Article 5.2 – Data protection demonstration based on manual processes is not efficient and sufficient.
    Only proactive or automatically reacting on correlated events solutions can cover GDPR requirements. DAM in addition to the SQL logging provides information about activity context (who, when, what), strong reporting capabilities to review analyzed incident quickly, policies which identify PI’s processing, quantitative analysis simplifies abnormal behave and self-learning engine discovering anomalies in standard access to monitored system.
    DAM blocking capabilities are unique to provide full control on privileged accounts and implement access control covering the segregation of duties demand.
  • Article 9 – Processing of special categories of personal data
    Sensitive personal data (racial or ethnic origins, political opinions, religious beliefs, genetic and biometric data and health condition or sexual orientation) included in PI’s administrator databases change strength of GDPR requirements in 2 places:

    • Article 30.5 – even company employs less that 250 workers, PI’s processing has to be recorded
    • Article 83.5(a) – administrative fines related to lack of compliance on silo with sensitive information are doubled

DAM data classification engine can identify sensitive information with minimum number of false positive results based on catalog, regular expression, dictionary or custom searches. Achieved results allow to focus on the most critical assets from GDPR perspective.
Classification and database discovery processes executed on schedule rapidly identify changes inside database schema and network assets.
Awareness where sensitive data are located is crucial to confirm efficiency of working processes for data pseudonymization and minimization.
Data lake monitoring can be implemented only in the largest corporation, the knowledge what should be protected is the first step before we spend the limited budget.

  • Article 24.1 and 24.2 – Data administrator duties
    These two articles impose a data protection obligation on the data controller as an auditable and controlled process. If we consider databases, data-warehouses, big data and file repositories the DAM was exactly created for this.
  • Article 28 – Data processor duties
    According to data processing on behalf of data controller (very common situation) the processor must guarantee that the access to PI’s takes place on written administrator authorization. Only data access monitoring can provide real access registry.
  • Article 30 – Records of processing activities – puts the requirement of the personal information access accountability
    Small companies will implement this goal by creating simple registry, based on manual data access description, sometimes enriched by approval workflow.
    However the low cost solution is tied with complexity of reporting and lack of non-repudiated registry so you should be considered better mechanism to register access to GDPR protected data.
  • Article 32.1(d) – Security of processing points vulnerability assessment and system hardening
    Popular platforms dealing with vulnerability assessment treat the relational databases harshly. DAM originated from RDBMS world provide rich checks and not only focus on CVE’s and standards (CIS, STIG). Based on years of experience it includes also analysis of SQL traffic, influence the configuration changes on the risk score, authorization snapshots and excessive rights identification.
    For most critical systems the DAM extension to existing VA solution in your environment can be very helpful.
  • Article 33.3(a) – Data breach notification imposes on the subject not only the requirement for immediate notification (3 days).
    Breach notification should contain information about scale of the leakage or other type of incident. Only DAM solutions can identify this scope (SQL audit) and minimize damages related with data owners notification and possible fines.
    Be aware that:

    • DLP’s (agent and network) covers only data on workstation and remote acceses. What about local session on servers, are you sure that your DLP provides this same SQL structure and session context analysis as specialized to this purpose DAM solutions?
    • PIM’s monitor access of privileged users to production systems. They are not aware of SQL syntax and session context. PIM should be considered in GDPR compliance program but the real value is visible when DAM and PIM are integrated together (directly or on SIEM level).
  • Article 34 – Communication of a personal data breach to the data subject
    Technically DAM solutions are able to parse output of SELECT’s but usability of this functionality is limited. The size of outgoing stream is unpredictable and can lead to situation that monitoring system should have more hardware resources that monitored one (especially on data-warehouse).
    However DAM can provide list of SQL instructions executed inside suspicious session and simplify the recognition of the attack range. In case of data modification (DML’s) audited SQL activity can directly identify changes and required remediation.

Does DAM provide protection for applications in GDPR context?

The 3-tier architecture of most applications (web client, application server, data store) anonymizes access to data on silo level. So we cannot identify application user on the SQL level basis only on the database user name which points the account from the pool of connections. However DAM can be configured to extract this information from SQL, JDBC encapsulation message, Web Server logs and other streams. In most cases this kind of integration requires additional implementation effort including in the worst case the application code change.
So, if the application user context is visible on DAM level we can utilize exactly it this same way like described earlier with two objections:

  • Never kill the session in the pool of connection because content of SQL stream inside belongs to many application users. Killed session will raise exceptions on application layer and reinitialize application session for thousands clients.
  • Never mask data or rewrite SQL in the pool of connection. Masked data in most cases will have inappropriate format and will lead to application exceptions. Even the masked data will have accepted format (data tokenization) the information receiver will not have idea about this fact and can made business or law decisions based on incorrect information – data masking for application should be implemented on application or presentation layer.
    The rewritten SQL inside SQL transaction can change it essence and leads to lost of data consistency.

DAM without application user context is still valuable in this stream to identify anomalies, errors, behavioral fluctuations using quantitative analysis.

Can DAM implement data pseudonymization?

Hmm, we should start from basic question – what pseudonymization is?
I saw many web articles which directly equals this word with data masking but I disagree with this approach.

GDPR defines pseudonymization as the processing of personal data in such a manner that the personal data can no longer be attributed to a specific data subject without the use of additional information, provided that such additional information is kept separately and is subject to technical and organizational measures to ensure that the personal data are not attributed to an identified or identifiable natural person.

I treat this definition as a consequence and continuation of the data minimization process. Briefly, if PI’s data will be separated from transactional ones (data minimization) the natural relation between these two stores (for example customerID) should be utilized in whole data process flow. Only on demand and with approval the customerID can be translated to form which identify the person.
Can DAM help here? – NOPE.
However the implementation of data minimization and pseudonymization for existing systems is tied with complete application redevelopment – who can afford it? So, only new GDPR-ready applications will come with this kind of functionality on board.

For existing systems we try to avoid the personal information identification using data masking and here DAM can be also helpful:

  • preproduction (test) data – why DAM instead of data tokenization?
  • access outside application stream:
    • masking of SELECT output – most DAM’s provides this functionality but efficiency is the main problem
    • query rewrite – very suitable and provides possibility to tokenize and encrypt data instead simple masking
  • access from application stream – what I mentioned earlier, application masking should be implemented on application or presentation layer only

Member states implementation of GDPR

GDPR is the regulation and unifies law in the European Union but in few regulation articles we can find some derogations. The good example is Article 9.4 where health records can be managed different way according to member state decision. Does it mean that my decision about scope and type of protection should be postponed until parliament implementation of the law?
Definitely, you should not wait because your data may contain personal information about citizens from another EU country and you can be sued based on his state law.

DAM and “right to be forgotten”

It is common question raised during DAM discussions.
The Article 17 introduces subject right to remove its personal information on request. DAM is monitoring solution and does not cooperate directly with DB engine (to cover SoD requirement) and has no authorization to modify data. So this simple explanation leads to only one correct answer on the titled question – DAM is not component which can be useful in case of implementation the citizen right to be forgotten.
By the way, who will agree with the data removal on system with thousands relations what can lead to lose data consistency which can be discovered one year later? I think that only new systems with fully implemented data minimization and psedonymization principles will be able to identify this right easy way. If all personal information are separated from transactions, the PI’s removal or simple encryption will provide suitable solution without any additional effort.

Administrative fines mantra

Have you seen any GDPR related article without remark about “huge fines up to 20 billions euro or 4% company turnover”?
Do you believe that your government will decide to kill local, average or small companies because of GDPR?
If your answers are negative you should consider much more interesting case. In the Article 82 the GDPR introduces citizen right to compensation with a body of appeal attached directly to EU Council. Many organizations seriously consider the costs of civil actions and its possible influence on business.

New type of ransomware
Standard ransomware based on data encryption is not efficient because victims pay rarely (privates are not able to pay large amount of money, backup exists, block of bitcoin account).
With GDPR the stolen data can be a simple way to force ransom from an organization wishing to avoid penalties and massive civil actions.
I think that data gathered actually from unaware companies are stored somewhere in the darknet to be starting package for new type of “business” next year. 😦


DAM definitely should be considered as the important element of any GPDR compliance program because of:

  • PI processing monitoring
  • data classification
  • data masking
  • unauthorized data access protection
  • vulnerability assessment

and achieves the best value when it is integrated with PIM, IAM, Encryption and SIEM.