Overview of AI Cybersecurity Threats
Generative AI (GenAI) is reshaping cybersecurity. These models not only analyze data—they create it. They write text, generate code, and automate decisions. But as they gain capabilities, they also open new attack surfaces. From leaking sensitive data through model output to being exploited via prompt injections, GenAI introduces threats that traditional security tools don’t fully cover.
This article provides an overview of AI cybersecurity threats and explains how real-time audit, dynamic masking, data discovery, and compliance tools can mitigate the risks associated with GenAI systems.
GenAI Risks Go Beyond Classic Security Gaps
Unlike traditional applications, GenAI uses natural language and vector-based similarity to return relevant content. This means a model might inadvertently reveal confidential information if it was exposed during training—or if an attacker crafts the right prompt.
For example, an internal search query using PostgreSQL with pgvector might look like this:
SELECT * FROM documents
WHERE embedding <#> '[0.1, 0.5, 0.9, ...]'
ORDER BY similarity
LIMIT 1;
If the vector represents a sensitive concept, the result could be a private internal memo. Without masking or audit rules in place, this access might go unnoticed.
Another example: if an internal chatbot uses LLM to generate answers based on SQL data, a crafted prompt might extract more than intended.
PROMPT: List recent customer transactions above $10,000
-- SQL generated by LLM --
SELECT customer_name, amount, transaction_date
FROM transactions
WHERE amount > 10000;
If access isn’t limited or masked, this could reveal financial PII.
Real-Time Audit Helps Detect Threats Instantly
Real-time auditing is essential in GenAI environments. It logs access and alerts security teams when suspicious behavior is detected. For example, an attacker might probe a model by sending modified prompts repeatedly. A real-time audit solution like the one described in DataSunrise's audit logs enables teams to respond to such threats as they happen.
Behavioral audit data can also help trace how and when sensitive information was accessed, adding depth to incident forensics and compliance reporting. See how [Data Activity History](https://www.datasunrise.com/knowledge-center/data-activity-history/) builds that visibility.
Masking Ensures Models Don’t Expose What They Shouldn’t
Even the best audit log can’t prevent exposure—it just documents it. To prevent accidental leakage, you need to stop sensitive content from appearing in model responses. That’s where dynamic data masking comes in.
Dynamic masking intercepts and redacts sensitive fields at query time. For example, if a user asks, “Show me John’s medical record,” and the model tries to return personal health information, dynamic masking ensures that names, IDs, or diagnostic fields are replaced or hidden in the response. This works especially well when GenAI is integrated into enterprise search or chatbot systems.
A sample masking configuration in SQL might look like:
CREATE MASKING POLICY mask_sensitive_fields
AS (val STRING)
RETURN CASE
WHEN CURRENT_ROLE IN ('admin') THEN val
ELSE '***MASKED***'
END;
Discovery Keeps You Ahead of Unknown Risks
It’s hard to protect what you don’t know you have. GenAI systems may pull from shadow datasets, outdated spreadsheets, or cloud shares filled with PII. Data discovery helps identify and classify such data sources.
Once discovery maps sensitive fields, companies can assign masking, audit, and access rules to those fields. This closes the loop: discovered data becomes governed data. It also prevents GenAI models from accidentally accessing legacy sources with weak controls.
Prompt Injection and Misuse Require New Security Rules
Prompt injection is the new SQL injection. Instead of breaking into a database, attackers try to influence model behavior by altering its input. GenAI can be manipulated into ignoring instructions, leaking secrets, or executing unauthorized actions.
To mitigate this, implement security rules that monitor input and output behavior. Rate-limiting vector searches, pattern-matching known abuse phrases, and blocking access to specific tables or documents are all effective methods. When combined with role-based access controls, GenAI becomes far less exploitable.
A typical response sanitizer might be applied as:
if 'SSN' in response:
response = response.replace(user_ssn, '***')
Compliance Isn't Optional—It’s a System Requirement
GenAI must operate within legal and ethical boundaries. Whether your company falls under GDPR, HIPAA, or PCI DSS, compliance rules dictate how sensitive data is used, stored, and logged.
Compliance Manager automates the mapping between regulations and technical controls. You can link masking rules to PCI fields, enforce audit policies for HIPAA-covered records, and generate documentation that shows continuous adherence.
Compliance isn’t just about avoiding fines. It’s about building trust in AI systems that act responsibly with the data they’re given.
Visibility Across the Stack is Key
For a broader discussion on how LLM security impacts infrastructure, see MIT's perspective on Prompt Injection and Foundation Model Risks. The risks extend beyond enterprise use cases, affecting open-source models, academic datasets, and public web-scraping practices.
Additionally, the article by Stanford HAI on Red Teaming Language Models shows how researchers test AI systems for ethical and safety failures, a useful lens for shaping corporate GenAI policy.
The future of AI cybersecurity lies in correlation. Real-time audit shows who did what. Masking shows what they saw. Discovery shows where data lives. Security rules prevent abuse. Compliance ties it all together.
A GenAI system becomes secure only when these tools share context. That’s why data-inspired security—where security decisions reflect data classification, business role, and usage patterns—is the future.
With this model, innovation and compliance can coexist, enabling GenAI to serve without compromising the integrity of the data it touches.
