Untervorgänge
| Bezeichnung | Beschreibung |
|---|---|
| AI Output / Decision Integrity Failure | An Artificial Intelligence (AI) model produces inaccurate, manipulated, biased, unsafe or otherwise unreliable outputs or decisions that cause dependent controls, users or processes to act incorrectly. |
| AI Data Confidentiality / Integrity / Provenance Loss | Training, fine-tuning, embedding, retrieval or inference data is disclosed, altered, contaminated, misattributed or stripped of required provenance, undermining data privacy, security or Artificial Intelligence (AI) reliability. |
| Unauthorized / Harmful Autonomous Action | An Artificial Intelligence (AI) agent or autonomous system takes an action outside authorized scope or intended objectives, causing security, data privacy, safety, legal, financial and/or operational harm. |
| Sensitive Data / Compliance Exposure from Unsanctioned AI Use | Unapproved or ungoverned Artificial Intelligence (AI) use: (1) Exposes sensitive and/or regulated data; (2) Creates unauthorized processing; (3) Introduces Intellectual Property (IP) or licensing issues; and/or (4) Causes statutory, regulatory and/or contractual noncompliance. |
| Unintended or Unsafe Technology Behavior | Artificial Intelligence (AI) exhibits emergent, unanticipated or unintended behavior that produces incorrect, unsafe or harmful outcomes or causes dependent controls and processes to operate outside intended boundaries. |
| AI Bias / Discriminatory Outcome | Artificial Intelligence (AI)-supported decisions or outputs produce unjustified, systematically biased or discriminatory outcomes that create legal, ethical, reputational or individual harm. |
| AI Output / Decision Integrity Failure | An Artificial Intelligence (AI) model produces inaccurate, manipulated, biased, unsafe or otherwise unreliable outputs or decisions that cause dependent controls, users or processes to act incorrectly. |
| AI Data Confidentiality / Integrity / Provenance Loss | Training, fine-tuning, embedding, retrieval or inference data is disclosed, altered, contaminated, misattributed or stripped of required provenance, undermining data privacy, security or Artificial Intelligence (AI) reliability. |
| Unauthorized / Harmful Autonomous Action | An Artificial Intelligence (AI) agent or autonomous system takes an action outside authorized scope or intended objectives, causing security, data privacy, safety, legal, financial and/or operational harm. |
| Sensitive Data / Compliance Exposure from Unsanctioned AI Use | Unapproved or ungoverned Artificial Intelligence (AI) use: (1) Exposes sensitive and/or regulated data; (2) Creates unauthorized processing; (3) Introduces Intellectual Property (IP) or licensing issues; and/or (4) Causes statutory, regulatory and/or contractual noncompliance. |
| Unintended or Unsafe Technology Behavior | Artificial Intelligence (AI) exhibits emergent, unanticipated or unintended behavior that produces incorrect, unsafe or harmful outcomes or causes dependent controls and processes to operate outside intended boundaries. |
| AI Bias / Discriminatory Outcome | Artificial Intelligence (AI)-supported decisions or outputs produce unjustified, systematically biased or discriminatory outcomes that create legal, ethical, reputational or individual harm. |
| AI Output / Decision Integrity Failure | An Artificial Intelligence (AI) model produces inaccurate, manipulated, biased, unsafe or otherwise unreliable outputs or decisions that cause dependent controls, users or processes to act incorrectly. |
| AI Data Confidentiality / Integrity / Provenance Loss | Training, fine-tuning, embedding, retrieval or inference data is disclosed, altered, contaminated, misattributed or stripped of required provenance, undermining data privacy, security or Artificial Intelligence (AI) reliability. |
| Unauthorized / Harmful Autonomous Action | An Artificial Intelligence (AI) agent or autonomous system takes an action outside authorized scope or intended objectives, causing security, data privacy, safety, legal, financial and/or operational harm. |
| Sensitive Data / Compliance Exposure from Unsanctioned AI Use | Unapproved or ungoverned Artificial Intelligence (AI) use: (1) Exposes sensitive and/or regulated data; (2) Creates unauthorized processing; (3) Introduces Intellectual Property (IP) or licensing issues; and/or (4) Causes statutory, regulatory and/or contractual noncompliance. |
| Unintended or Unsafe Technology Behavior | Artificial Intelligence (AI) exhibits emergent, unanticipated or unintended behavior that produces incorrect, unsafe or harmful outcomes or causes dependent controls and processes to operate outside intended boundaries. |
| AI Bias / Discriminatory Outcome | Artificial Intelligence (AI)-supported decisions or outputs produce unjustified, systematically biased or discriminatory outcomes that create legal, ethical, reputational or individual harm. |
- Secure Controls Framework -
The Secure Controls Framework® (SCF)
"The SCF is the Common Controls Framework™ (CCF), the world's most comprehensive cybersecurity and data privacy metaframework - it is also free to use. The entire concept is building secure, compliant and resilient capabilities in the most efficient and cost-effective manner possible.
The SCF is more than just a unified control catalog, since its included content creates a playbook for Governance, Risk & Compliance (GRC) capabilities. Used globally by organizations of every size, the SCF is a robust and scalable solution for security, compliance and resilience controls. As a comprehensive security framework, the SCF maps 1,400+ controls across 200+ laws, regulations, and industry frameworks so you can implement once and comply everywhere.
Like it or not, cybersecurity is a protracted war on an asymmetric battlefield, where the threats are everywhere and as defenders we have to make the effort to work together to help improve cybersecurity and data privacy practices, since we all suffer when massive data breaches occur or when cyber attacks have physical impacts. Hackers share information on attack methods with other hackers, so why shouldn’t the good guys share information on how to best protect an organization? We decided to take action and make a difference, since we feel it is too important to wait for someone else to fix the problems that exist.
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/Terms & Conditions
The SCF End User License Agreement (EULA) governs the use of the Secure Controls Framework® (SCF) under the Creative Commons Attribution-No Derivatives 4.0 International Public License.