+Automated Decision-Making Technology (ADMT) For Data Subject Actions

Automated Decision-Making Technology (ADMT) For Data Subject Actions

Description

Mechanisms exist to ensure data subject actions utilizing Automated Decision-Making Technology (ADMT) where computation replaces, or substantially replaces, human decisionmaking, conforms with all applicable statutory, regulatory and/or contractual obligations.

Possible Solutions & Considerations

Micro-Small Business (<10 staff) / BLS Firm Size Classes 1-2

∙ Inform users when automated decisions are made about them

Small Business (10-49 staff) / BLS Firm Size Classes 3-4

∙ ADMT disclosure policy in privacy notice

Medium Business (50-249 staff) / BLS Firm Size Classes 5-6

∙ Formal ADMT governance policy
∙ Disclosure requirements for automated decision-making

Large Business (250-999 staff) / BLS Firm Size Classes 7-8

∙ Enterprise ADMT governance program
∙ Transparency requirements
∙ Impact assessments

Enterprise (> 1,000 staff) / BLS Firm Size Class 9

∙ Enterprise ADMT governance framework
∙ Automated disclosure mechanisms
∙ GDPR Art. 22 and CCPA compliance
∙ DPIA program

SCR-CMM

Level 0 Not Performed

Practices are non-existent, based on the inability to demonstrate an implemented and operational capability. A reasonable person would conclude the control is not being performed.

Level 1 Performed Informally

SCR-CMM Level 1 criteria definitions are not available for this control:
▪ A reasonable person would conclude this control requires a structured process.
▪ At this level of maturity, the "ad hoc" nature of performing a capability informally would indicate the intent of the control is not met due to a lack of consistency and formality.

Level 2 Planned Tracked

SCR-CMM Level 2 criteria definitions are not available for this control:
▪ A reasonable person would conclude a well-defined and standardized process is required.
▪ At this level of maturity, the “requirements-driven” nature of performing the control is focused on a localized and/or regionalized implementation, not uniform and consistent across the organization.
▪ Requirements are narrowly scoped for applicability and are primarily derived from compliance obligations (e.g., laws, regulations and contracts).

Level 3 Well Defined

Privacy (PRI) capabilities are standardized across the entity for applicability to People, Processes, Technologies, Data and/or Facilities (PPTDF) to ensure consistency for Technology Assets, Applications, Services and/or Data (TAASD). Capability criteria associated with this control reasonably expect the following criteria to exist:
▪ Policies and standards associated with PRI domain capabilities are formally documented and centrally-managed by the entity's Governance, Risk & Compliance (GRC) team, or similar function.
▪ Standardized Operating Procedures (SOP) associated with PRI domain capabilities are well-documented and kept current by process owners.
▪ A data privacy team, or similar function, is appropriately staffed and supported to implement and maintain PRI domain capabilities.
▪ Technology is leveraged to enhance the efficiency and accuracy of data privacy operations (e.g., privacy notice management software and customer management solution).
▪ The entity's Governance, Risk & Compliance (GRC) team, or similar function, works with business stakeholders and process owners to appropriately scope and reasonably implement cybersecurity and data protection controls associated with PRI domain capabilities to address Minimum Compliance Requirements (MCR) (e.g., applicable statutory, regulatory and/or contractual requirements) and Discretionary Security Requirements (DSR) (e.g., entity-required controls).
▪ An implemented and operational capability exists to ensure data subject actions utilizing Automated Decision-Making Technology (ADMT) where computation replaces, or substantially replaces, human decisionmaking, conforms with all applicable statutory, regulatory and/or contractual obligations.

Level 4 Quantitatively Controlled

Utilize SCR-CMM Level 3 criteria definitions:
▪ There are no defined Level 4 criteria, since it is reasonable to assume a quantitatively-controlled process is not necessary to operationalize this control.
▪ While it may be possible to develop “metrics-driven” capabilities for this control, the criteria would be organization-specific to define.

Level 5 Continuously Improving

Utilize SCR-CMM Level 3 or Level 4 (if available) criteria definitions:
▪ There are no defined Level 5 criteria, since it is reasonable to assume a continuously-improving process is not necessary to operationalize this control.
▪ Level 5 capabilities should be considered “world-class” where the control builds on Level 4 capabilities, but are continuously improving through Artificial Intelligence (AI) and/or Machine Learning (ML) technologies.
▪ While it may be possible to develop responsive capabilities for this control through the use of AI and/or ML technologies, the criteria would be organization-specific to define.

1. Ħarsa ġenerali

Titlu Standard

1.1 Referenzi

1.2 Rekwiżiti identifikati

1.3 Regolamenti relatati

2. Rekwiżiti identifikati

Rekwiżiti
Sors Rekwiżit

3. Regolamenti relatati

Regolamenti
Sors Regolamentazzjoni
DELAW BDSG § 37 Automatisierte Entscheidungen im Einzelfall einschließlich Profiling

(1) Das Recht gemäß Artikel 22 Absatz 1 der Verordnung (EU) 2016/679, keiner ausschließlich auf einer automatisierten Verarbeitung beruhenden Entscheidung unterworfen zu werden, besteht über die in Artikel 22 Absatz 2 Buchstabe a und c der Verordnung (EU) 2016/679 genannten Ausnahmen hinaus nicht, wenn die Entscheidung im Rahmen der Leistungserbringung nach einem Versicherungsvertrag ergeht und

1.
dem Begehren der betroffenen Person stattgegeben wurde oder
2.
die Entscheidung auf der Anwendung verbindlicher Entgeltregelungen für Heilbehandlungen beruht und der Verantwortliche für den Fall, dass dem Antrag nicht vollumfänglich stattgegeben wird, angemessene Maßnahmen zur Wahrung der berechtigten Interessen der betroffenen Person trifft, wozu mindestens das Recht auf Erwirkung des Eingreifens einer Person seitens des Verantwortlichen, auf Darlegung des eigenen Standpunktes und auf Anfechtung der Entscheidung zählt; der Verantwortliche informiert die betroffene Person über diese Rechte spätestens zum Zeitpunkt der Mitteilung, aus der sich ergibt, dass dem Antrag der betroffenen Person nicht vollumfänglich stattgegeben wird.

(2) Entscheidungen nach Absatz 1 dürfen auf der Verarbeitung von Gesundheitsdaten im Sinne des Artikels 4 Nummer 15 der Verordnung (EU) 2016/679 beruhen. Der Verantwortliche sieht angemessene und spezifische Maßnahmen zur Wahrung der Interessen der betroffenen Person gemäß § 22 Absatz 2 Satz 2 vor.

DELAW BDSG § 54 Automatisierte Einzelentscheidung

(1) Eine ausschließlich auf einer automatischen Verarbeitung beruhende Entscheidung, die mit einer nachteiligen Rechtsfolge für die betroffene Person verbunden ist oder sie erheblich beeinträchtigt, ist nur zulässig, wenn sie in einer Rechtsvorschrift vorgesehen ist.

(2) Entscheidungen nach Absatz 1 dürfen nicht auf besonderen Kategorien personenbezogener Daten beruhen, sofern nicht geeignete Maßnahmen zum Schutz der Rechtsgüter sowie der berechtigten Interessen der betroffenen Personen getroffen wurden.

(3) Profiling, das zur Folge hat, dass betroffene Personen auf der Grundlage von besonderen Kategorien personenbezogener Daten diskriminiert werden, ist verboten.

Kwistjonijiet marbuta

Links
Tip ta’ link Kwistjoni
is related to Annual
is related to relative Control Weighting = 06
is related to Process
is related to Protect
is related to SCRM Focus Tier 2 OPERATIONAL
blocks Contractual Default / Termination
blocks Reputational Harm / Loss of Stakeholder Trust
blocks Regulatory / Legal / Contractual Financial Liability
blocks Loss of Authorization / Certification / License
blocks Uncontrolled Internal Security / Privacy Exposure
blocks Inability to Demonstrate Due Diligence / Due Care
blocks AI Output / Decision Integrity Failure
blocks Unauthorized / Harmful Autonomous Action
blocks Sensitive Data / Compliance Exposure from Unsanctioned AI Use
blocks AI Bias / Discriminatory Outcome
blocks Privacy Harm / Unauthorized Processing
blocks Drought & Water Shortage
blocks Earthquakes
blocks Fire & Wildfires
blocks Floods
blocks Hurricanes & Tropical Storms
blocks Landslides & Debris Flow
blocks Pandemic (Disease) Outbreaks
blocks Severe Weather
blocks Space Weather
blocks Thunderstorms & Lightning
blocks Tornadoes
blocks Tsunamis
blocks Volcanoes
blocks Winter Storms & Extreme Cold
blocks Malicious Cyber Activity
blocks Dysfunctional Management Practices
blocks Human Error
blocks Technical / Mechanical Failure
blocks Statutory / Regulatory / Contractual Change
blocks Data Quality Degradation / Data Poisoning
blocks Artificial Intelligence & Autonomous Technologies (AAT) System Failure or Unintended Behavior
blocks Fraud, Corruption and/or Intentional Criminal Conduct
blocks Conflict of Interest (COI) / Undue Influence / Insider Misconduct
blocks Workforce Shortage / Loss of Critical Skills / Labor Disruption
blocks Adversarial Artificial Intelligence (AI) Attack
is blocked by PRI-36.1 Automated Decision-Making Technology (ADMT) For Data Subject Actions – Assessment Procedure
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    The SCF is made up of volunteers, mainly specialists within the cybersecurity profession, who focus on GRC and the cybersecurity side of data privacy. These are auditors, engineers, architects, incident responders, consultants and other specialists who live and breathe these topics on a daily basis. The end product is "expert-derived content" that makes up the SCF." https://securecontrolsframework.com/ 

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