AI risk belongs in the boardroom.
A useful discussion starts with decisions and consequences, rather than a list of model vulnerabilities.
Read the field noteAI security · adversarial resilience
Protect the
intelligence.
Preserve the
decision.
AI can be manipulated. Its authority can be misused. We help leaders understand the exposure—and build the boundaries that matter.
Independent perspective. Business-focused security.
ARTIFICIALCOUNTERINTELLIGENCE / System boundary
● Concept view
Intelligence needs a perimeter.
Observe / Bound / Verify
Built for the decisions ahead
Executives & boards
Security leaders
AI & cloud architects
Enterprise teams
01 / The mission
More intelligence.
More to defend.
The next security boundary runs through the way your organization thinks, decides, and acts.
When AI reads enterprise data, advises employees, or operates tools, it becomes part of the business. Attackers may seek to influence those systems through the very content they are built to process.
Artificial Counterintelligence brings a defensive lens to that shift: examine adversarial influence, protect decision integrity, and keep automated action within clear limits.
Explore three paths to failure02 / The threat landscape
Follow the influence.
Find the exposure.
Three lenses for examining where AI systems can fail. Select a lens to explore an illustrative scenario.
Untrusted content → trusted context
A document is data.
A document is data.
Until it starts giving orders.
An assistant retrieves a document containing instructions aimed at redirecting its response. The risk grows when the application treats that content as authoritative.
01
A retrieved document contains hidden instructions
02
The assistant is asked to follow those instructions
03
Defensive boundary: keep content separate from authority
Illustrative scenario. This is a conceptual walkthrough, not a live security test.
Flawed evidence → business decision
A confident answer can still be the wrong answer.
An AI workflow produces a recommendation from incomplete or manipulated information. A consequential decision follows without checking the evidence or its provenance.
01
Incomplete or manipulated evidence enters the workflow
02
A recommendation is accepted without independent checks
03
Defensive boundary: verify evidence before consequential decisions
Illustrative scenario. This is a conceptual walkthrough, not a live security test.
Delegated authority → real-world action
The agent has a goal. How much authority does it need?
An agent acts on a misunderstood instruction using tools with excessive permissions. A bounded task becomes an unintended operational change.
01
An agent receives an ambiguous or manipulated task
02
Broad tool permissions allow an unintended change
03
Defensive boundary: limit scope and gate consequential actions
Illustrative scenario. This is a conceptual walkthrough, not a live security test.
03 / How we help
Clarity first.
Controls next.
Focused engagements that connect AI architecture to business risk. Open a service to see its scope.
01
AI security assessment
Understand the exposure before expanding adoption.
AI security assessment
Understand the exposure before expanding adoption.
Review how AI enters your environment: applications, data sources, identities, integrations, and decision workflows. Identify the places where untrusted content can influence privileged behavior.
What you take awayA prioritized exposure map, control gaps, and a practical remediation roadmap.
Discuss this engagement
02
Adversarial resilience review
Challenge the assumptions behind your safeguards.
Adversarial resilience review
Challenge the assumptions behind your safeguards.
Define authorized test scenarios for prompt manipulation, retrieval contamination, information disclosure, and unsafe tool use. Evaluate the application and its permissions alongside model behavior.
What you take awayA scenario-based review with evidence, impact analysis, and recommended control improvements.
Discuss this engagement
03
Agentic AI governance
Put boundaries around autonomous action.
Agentic AI governance
Put boundaries around autonomous action.
Examine delegated permissions, tool access, approval gates, monitoring, and recovery paths. Define when agents may act independently and when a person must intervene.
What you take awayAn operating model for agent authority, escalation, and accountability.
Discuss this engagement
04
Executive & board briefings
Translate AI exposure into business decisions.
Executive & board briefings
Translate AI exposure into business decisions.
Connect technical failure paths to operational consequences. Examine the decisions leaders need to make about adoption, risk ownership, spending, and oversight.
What you take awayA focused briefing with decision questions and recommended next steps.
Discuss this engagement04 / The approach
Make the risk visible.
Make the response practical.
Start with a defined system and business context. End with decisions your team can act on.
01 / Map
Find the boundaries.
Trace inputs, identities, data sources, tools, and the decisions AI is allowed to influence.
02 / Challenge
Examine the failure paths.
Review how adversarial influence could cross those boundaries, within an agreed scope.
03 / Prioritize
Connect risk to impact.
Translate findings into business consequences, accountable owners, and control priorities.
04 / Strengthen
Build a defensible response.
Define safeguards, escalation, and verification steps your teams can operate.
The intended outcome
A clear exposure map. A prioritized roadmap. A better basis for AI decisions.
05 / Field notes
Think beyond the model.
Perspectives on securing the systems, decisions, and authority around AI.
An intelligent agent still needs limits.
Permission, approval, and recovery should be designed before autonomy expands.
Read the field noteWhen content becomes command.
Why AI applications need a firm boundary between information to read and instructions to follow.
Read the field note06 / The perspective
Physics-trained.
Architecture-tested.
Security-focused.
Led by Anuj Varma, a cloud, enterprise, and security architect with more than 30 years in technology consulting.
The perspective combines systems thinking with enterprise experience across cloud architecture, security, and AI. The goal is to help leaders ask better questions—and turn the answers into practical action.
Anuj Varma
Architect · Author · Executive advisor
07 / Questions, answered
A good place
to begin.
What does “artificial counterintelligence” mean?
Here, it means examining and resisting adversarial influence on AI systems: how information enters, how decisions form, and how actions are authorized. It is the organizing idea behind this site.
Is this relevant if we only use AI assistants?
Yes. Assistants can interact with sensitive information and shape employee decisions even without taking autonomous actions. The assessment scope should reflect what your particular assistant can access and influence.
How does an engagement begin?
With an exploratory conversation about your AI use cases, architecture, concerns, and desired outcomes. Scope and deliverables are agreed before assessment work begins.
Do you provide a security product?
The focus is advisory and assessment work. We examine your operating context and controls, then recommend practical improvements suited to your environment.
Will an assessment certify that our AI is safe?
No assessment can guarantee safety in every context. It provides evidence about the reviewed scope, identifies gaps, and supports ongoing verification as systems and usage change.
The next decision starts here
What is your AI
allowed to influence?
Let’s explore where AI touches your business—and where clearer boundaries could make a difference.