At a Senate Armed Services Committee hearing yesterday on foreign cyber threats to the U.S., there were several references to the saying that “people who live in glass houses should not throw stones.” The point, made by DNI James Clapper, was that the U.S. should not be too quick to penalize the very espionage practices that U.S. intelligence agencies rely upon, including clandestine collection of information from foreign computer networks.
But perhaps a more pertinent saying would be “It takes a thief to catch a thief.”
U.S. intelligence agencies should be well-equipped to recognize Russian cyber threats and political intervention since they have been tasked for decades to carry out comparable efforts.
A newly disclosed intelligence directive from 1999 addresses “information operations” (IO), which are defined as: “Actions taken to affect adversary information and information systems while defending one’s own information and information systems.”
“Although still evolving, the fundamental concept of IO is to integrate different activities to affect [adversary] decision making processes, information systems, and supporting information infrastructures to achieve specific objectives.”
The elements of information operations may include computer network attack, computer network exploitation, and covert action.
See Director of Central Intelligence Directive 7/3, Information Operations and Intelligence Community Related Activities, effective 01 July 1999.
The directive was declassified (in part) on December 2 by the Interagency Security Classification Appeals Panel, and was first obtained and published by GovernmentAttic.org.
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.
When properly structured — with specific numeric targets, secured financial obligations, independent monitoring, and meaningful enforcement — CBAs transform data center deals into durable community partnerships.