Update 3_development_time_threats.md

Author: disesdi

content/ai_exchange/content/docs/3_development_time_threats.md

@@ -325,7 +325,7 @@ Useful standards include:
 Data quality control: Perform quality control on data including detecting poisoned samples through integrity checks, statistical deviation or pattern recognition. 
 
 Particularity for AI: Standard data quality checks are not sufficient for AI systems, as data may be maliciously altered to compromise model behavior. This requires different checks than standard checks on quality issues from the source, or that occured by mistake. Nevertheless, standard checks can help somewhat to detect malicious changes. It is essential to implement enhanced security measures to detect these alterations:
-- Secure Hash Codes: Safely store hash codes of data elements, such as images, and conduct regular checks for manipulations. See [DEVSECURITY](/goto/devsecurity) for more details on integity checks.
+- Secure Hash Codes: Safely store hash codes of data elements, such as images, and conduct regular checks for manipulations. See [DEVSECURITY](/goto/devsecurity) for more details on integrity checks.
 - Statistical deviation detection
 - Recognizing specific types of poisoned samples by applying pattern recognition