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Nchekwa AI

AI security protects models, data, tools, and surrounding services from unauthorized access or manipulation.

2 nkeji na-agụEmelitere ikpeazụ Part of the AI Policy & Society learning path

Nchịkọta

It includes ordinary software security and threats that target learning or model behavior. A secure design begins with the assets, adversaries, and trust boundaries of the actual application.

Isi ihe na-ewe

  • Threat-model the full application.
  • Enforce permissions outside the model.
  • Retest controls across system changes.

Ime miri emi

Identify what needs protection: private inputs, training data, model artifacts, credentials, connected accounts, and external actions. Record who can influence each input and what an attacker could gain from a failure. A public chatbot and an internal agent with write access have different threat models. Threats can affect different stages. Poisoned training material can alter learned behavior; adversarial inputs can manipulate predictions; untrusted retrieved content can redirect a tool-using application. Model output can also become dangerous when inserted into a database query, webpage, or command without appropriate handling. Apply controls at the software boundary. Enforce authorization in code, keep secrets out of model-visible context where possible, restrict tool scope, and validate outputs before use. A prompt asking a model to behave safely cannot replace account isolation or permission checks. Test representative failure paths in an authorized environment and maintain an incident process. Log enough information to investigate without collecting unnecessary sensitive content. Evaluate controls after changes to the model, retrieval sources, tools, and dependencies. Describe residual risk honestly; no single filter establishes complete protection.

Nghọta nka nka

A model refusing one malicious prompt does not prove that a system is secure. Different inputs, tools, modalities, and component boundaries can create distinct failure paths.

Locate the security boundary

  1. Imagine an assistant searching a private document store for a signed-in user.
  2. Apply the user’s access filter in the retrieval service before documents enter the model context.
  3. Test with a document belonging to a different account and verify that neither its contents nor identifying metadata appear in the result.

This defensive, hypothetical test checks authorization independently of the model’s willingness to follow instructions.

Mmetụta atụmatụ

Ihe ize ndụ na nchekwa

Ọdachi na mmerụ AI kwa ụbọchị dabere na onye ghọtara ihe egwu dị na onye nwere ike ime ihe.

Mkpebi doro anya

mmuta nke ọha na nke ọkachamara na-akpụzi ma amụma nchekwa siri ike ọ ga-ekwe omume na ndọrọ ndọrọ ọchịchị.

Ịcha site hype

Nkọwa doro anya na-ebelata njide site na hype, ụlọ nyocha PR na ụlọ ihe nkiri na-edoghị anya.

Mmejuputa n'ezie n'ụwa

Check that one account cannot retrieve another account’s documents.

Validate generated fields before using them in a database operation.

Ihe ize ndụ & okporo ụzọ nche

Ịgwọ ihe egwu dị adị dị ka sci-fi mgbe ike ogige.

Nchekwa ngwaahịa elu na-agbagwoju anya yana itinye n'okpuru ikike dị elu.

Hapụ ndị na-abụghị ndị bekee na ndị ọkachamara nwere naanị isi mmalite dị ala.

Map mmejuputa

1

Mmebi ngwaahịa dị iche iche, iji ya eme ihe na enweghị njikwa / ihe egwu adịghị mma.

2

Jụọ ihe akaebe ga-agbanwe echiche gị na usoro iheomume na ịdị njọ.

3

Na-ahọrọ isi mmalite na nyocha pụtara ìhè karịa nzọrọ ahịa.

4

Chọpụta otu ụzọ omume: ọrụ, amụma, ego, ma ọ bụ nka - ọ bụghị naanị mmata.

Isi mmalite na ịgụkwu ihe

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Ajụjụ a na-ajụkarị

Is a strong system prompt enough to secure an assistant?

No. Authentication, authorization, input and output handling, tool limits, and incident response remain necessary parts of the application.