The Department of Defense has issued a new Instruction defining its response to the “insider threat” from Department personnel who engage in unauthorized disclosures of information or other activities deemed harmful to national security.
The new Instruction assigns responsibilities and authorities for systematically detecting “anomalous” employee behavior that may be an indication of an insider threat.
An insider threat is defined as “A person with authorized access, who uses that access, wittingly or unwittingly, to harm national security interests or national security through unauthorized disclosure, data modification, espionage, terrorism, or kinetic actions resulting in loss or degradation of resources or capabilities.”
A subset of the insider threat is the counterintelligence (CI) insider threat, which refers to an authorized individual who uses his access on behalf of a “foreign intelligence entity.”
A foreign intelligence entity (FIE) is “Any known or suspected foreign organization, person, or group (public, private, or governmental) that conducts intelligence activities to acquire U.S. information, blocks or impairs U.S. intelligence collection, influences U.S. policy, or disrupts U.S. systems and programs.”
All heads of DoD components are now instructed to “implement CI insider threat initiatives to identify DoD-affiliated personnel suspected of or actually compromising DoD information on behalf of an FIE.”
All military departments are expected to “conduct anomaly-based detection activities.”
See “Countering Espionage, International Terrorism, and the Counterintelligence (CI) Insider Threat,” DoD Instruction 5240.26, May 4, 2012.
The new Instruction complies with a congressional mandate in the FY2012 defense authorization act that was passed last year in response to the WikiLeaks disclosures.
Americans are paying too much for almost everything, because the United States has long treated its trucking industry as an artifact to be preserved rather than as an opportunity for innovation.
These ideas aim to advance the detailed policy solutions needed to foster public trust and implement fairness in the adoption of AI across diverse domains, from healthcare and government benefits to rural access, education, and worker protections.
The evidence is clear: algorithmic pay-setting is established in app-based work, and payroll/timekeeping failures show how software can produce systemic wage harm at scale
While a few states have taken steps to implement decision-making mechanisms for certain AI systems, too many leaders are simply accepting narratives about AI’s purported public benefit at face value – jumping to the “how” of AI implementation before thoroughly vetting potential systems and deciding whether they are appropriate to use at all.