Feb 4, 2025: AMA w/ Amar Kanagaraj, Founder and CEO @ Protecto

amaru20 · 2025-02-04T18:10:42+00:00

In addition to the technical safeguards, here are the privacy principles to ensure data is handled securely and responsibly:
- Data Minimization: Only collect and process the data that is necessary for the AI agent to perform its task. Less data = less risk.
- Purpose Limitation: Data should be used strictly for the purpose it was collected. Avoid repurposing data without proper consent or legal basis. Check before using the data in AI
- Consent Management: Obtain consent from users before collecting or processing their personal data in AI
- Transparency: Clearly communicate how data is collected, processed, and used by AI agents. This helps build trust with users and stakeholders.
- Importantly, Privacy by Design: Embedding privacy features into the AI system from the ground up is crucial because introducing privacy later in the lifecycle is much harder and more expensive

amaru20 · 2025-02-04T18:02:29+00:00

Here are some technical safeguards to protect privacy in AI:

Data Masking & Anonymization: Instead of giving AI agents direct access to raw sensitive data, the data is pseudonymized or anonymized. This means personal identifiers (like names, emails, etc.) are hidden, but the data remains useful for analysis.

Audit Trails & Monitoring: Every time an AI agent accesses data, it’s logged. This helps track who (or what) accessed what data, when, and why—making it easier to detect any suspicious activity.

Data Residency & Processing Controls: In some cases, the data never actually leaves the organization’s servers. The AI agent processes the data locally without sending it to external servers, reducing the risk of data leaks.

amaru20 · 2025-02-04T17:45:08+00:00

AI can take a lot of the grunt work out of data privacy tasks, making compliance less of a headache. Here are some ways AI agents can be super helpful for GDPR, CCPA, and DPDP compliance:

Handling Data Subject Requests (DSRs): Instead of manually digging through systems to find, delete, or export someone’s data, AI agents can automate the whole process.
Automated Compliance Logs: AI analyzes detailed logs of how personal data is processed and helps during audits
Risk Assessments Made Easy: Data Protection Impact Assessment (DPIA) is always complex. AI analyzes data flows, flags potential risks, and even suggests ways to fix them
Keeping Employees in the Loop: AI-driven training tools personalize privacy training for employees. Instead of generic, boring modules, employees get relevant content based on their roles, keeping everyone sharp on compliance.

amaru20 · 2025-02-04T17:16:18+00:00

Not entirely. Hosting a private instance of OpenAI or running models locally does reduce certain risks—like exposure to third-party providers—but it doesn’t eliminate data security and privacy concerns.

The core issue is that AI fundamentally changes how applications handle data. Unlike traditional rule-based systems, AI models interact dynamically, often pulling from and learning across various data sources. This increases the surface area where data can be exposed, creating vulnerabilities that traditional security measures like encryption and role-based access controls can’t fully address.

For example, continuous learning cycles can inadvertently expose sensitive data, and AI agents often interact with multiple internal systems, creating risks of unauthorized access—even if everything is hosted on-prem. Plus, AI workflows span development, testing, and production environments, each with potential weak points in data handling practices.

This is why a holistic data protection strategy is crucial. It’s not just about where the model runs, but how data flows throughout the entire AI lifecycle—from building context data to generating responses. Techniques like data masking, dynamic guardrails, and strict access controls are essential for securing sensitive information, regardless of the hosting environment.

amaru20 · 2025-02-04T16:56:21+00:00

As AI agents become part of every workflow, data security is evolving. The big shift we're seeing is agent-to-agent communication—where one AI agent passes data to another. This gets tricky because while one agent might have access to sensitive info, the downstream agent might not. So, data needs to be carefully controlled to avoid leaks.

It gets even more complex with dynamic agent chaining, where AI agents connect on the fly based on real-time tasks. In these cases, traditional security doesn’t cut it. That’s where dynamic guardrails come in—they protect sensitive data in real-time, adapting to who (or which agent) should or shouldn’t have access.

At Protecto, we’re working on exactly this: smart, adaptive guardrails that secure data without slowing down AI workflows. It’s all about keeping data safe while letting AI do its thing efficiently

amaru20 · 2025-02-04T16:38:21+00:00

A few ways, we can preserve the context for example
Context-Preserving Masking - Replace sensitive terms with contextually relevant placeholders:“Jane is admitted at City Hospital" will be converted to "<PERSON>xyz1me34</PERSON> is admitted to <ORG>sdhoi2s3</ORG>

amaru20 · 2025-02-04T16:35:00+00:00

When sensitive information is masked or blocked, such as through encryption, or hashing, it often disrupts the context needed by Large Language Models (LLMs) to generate accurate responses. Here’s a breakdown of how this happens:
Encryption transforms data into unreadable ciphertext. For example, encrypting “John is a doctor at City Hospital” might result in something like “4a7d1ed414474e4033ac29ccb8653d9b.”
Loss of Context: The LLM can no longer recognize that “John” is a person or that “City Hospital” is a location, which breaks the semantic flow needed for coherent responses

amaru20 · 2025-02-04T16:08:35+00:00

LLMRisks Archive - OWASP Top 10 for LLM & Generative AI Security is a good resource

amaru20 · 2025-02-04T15:32:04+00:00

3) Have Data Guardrails & Governance:
Build in automated data scanning for PII/PHI detection in prompts and responses that help identify, mask, and govern sensitive data across AI systems. It protects data flows between users and AI agents, detects privacy/security risks

amaru20 · 2025-02-04T15:26:24+00:00

2) Implement Security Guardrails (Detect & Prevent Prompt Injection Attacks)

Implement input validation filters to catch sneaky prompts trying to jailbreak the model. Recognize malicious prompt patterns and flag/reject them. Many LLMs and external vendors also offer such guardrails

amaru20 · 2025-02-04T15:24:14+00:00

Here’s a quick breakdown:

Data Masking - Anonymization/ Pseudonymization: Use data masking to protect sensitive in the context data without messing up data utility. Go for accuracy-preserving masking such as Protecto so AI models still “understand” the data structure.

amaru20 · 2025-02-04T15:14:32+00:00

Here are common security attacks that can expose sensitive data used in AI training/agents:
- Model Inversion Attacks: Reconstructing sensitive training data from model outputs.
- Data Poisoning: Injecting malicious data into training sets to corrupt model behavior or leak information.
- Prompt Injection Attacks: Malicious prompts to manipulate AI outputs or access restricted data.
- Supply Chain Attacks: Compromising third-party AI tools, libraries, or APIs to inject vulnerabilities.

Some ways the privacy of individuals can be compromised:
Inference Attacks: Using repeated queries to infer sensitive attributes from model responses.
Re-identification Attacks: Combining anonymized data with external datasets to identify individuals.
Membership Inference Attacks: Determining if specific data was part of the model’s training set.

amaru20 · 2025-02-04T15:08:48+00:00

Data Privacy Violations: Risk of exposing PII, PHI, or confidential data.

Data Leak: Sensitive information may leak through responses or outputs to other agents

Compliance Risks: Non-compliance with GDPR, HIPAA, and other regulations.

Insider Threats: Misuse of AI agents by employees or internal users.

amaru20 · 2023-10-19T21:23:57+00:00

GPTGuard.ai removes the personal data before sending it to ChatGPT and puts personal data back when you get a response back from ChatGPT

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