Prompt injection attacks
Malicious instructions embedded in user inputs, documents, or external data sources designed to override your AI system's original behaviour and make it act against your interests.
SaaviAIDR
Your AI Is Running. Do You Know What It's Doing?
Purpose-built threat detection and response for AI-powered applications — because traditional security tools weren't designed for this.
Contact UsDetection Coverage
What SaaviAIDR detects.
Where traditional tools see a network packet, SaaviAIDR sees a prompt. Where traditional tools see an API call, SaaviAIDR sees a tool invocation by an agent and asks: was that expected? Was it authorised? What data did it touch?
Jailbreak attempts
Structured attempts to bypass your AI's safety guardrails and policy controls — forcing it to produce outputs or take actions it was explicitly designed to refuse.
Agent misuse
Unauthorised or out-of-scope actions taken by AI agents — including unexpected tool calls, privilege escalation, and operations that fall outside the agent's intended mandate.
Data leakage through AI
Detection of sensitive data — PII, credentials, internal documents, confidential context — being exposed through AI outputs, whether by manipulation or misconfiguration.
Rogue agent behaviour
Identification of agents operating outside expected parameters, pursuing unintended goals, or behaving in ways inconsistent with their design and deployment context.
Unsafe tool execution
Monitoring of every tool your AI agents call — flagging dangerous, unexpected, or policy-violating executions before they cause downstream damage.
Model manipulation
Detection of attempts to influence model behaviour through adversarial inputs, context poisoning, or indirect prompt injection through data the model is asked to process.
The AIDR Audit Trail
Security without visibility is just hope.
SaaviAIDR gives you a complete, queryable audit trail of every AI interaction in your environment — the foundation of AI governance and demonstrating to auditors, boards, and regulators that your AI systems are operating within defined boundaries.
Who used AI?
Full user and session attribution
Which model was called?
Model identity, version, and endpoint
What prompts went in?
Full prompt capture and classification
What came back out?
Response logging and content analysis
What tools were called?
Agent tool invocation audit trail
What actions were taken?
Downstream action tracking
What data was exposed?
Sensitive data detection and flagging
Why Traditional Tools Fall Short
Your existing security stack was built for a pre-AI world.
Firewalls don't inspect prompts. SIEMs don't understand agent behaviour. DLP tools can't detect when a model has been manipulated into leaking data it was never supposed to touch. AI attacks are different in kind — not just degree. SaaviAIDR fills that gap and integrates with your existing SOC and SIEM where relevant.
| Capability | SIEM / SOC | DLP | SaaviAIDR |
|---|---|---|---|
| Prompt-level visibility | No | No | Yes |
| Agent behaviour monitoring | No | No | Yes |
| Jailbreak detection | No | No | Yes |
| Tool execution auditing | No | No | Yes |
| AI-specific threat intelligence | No | No | Yes |
| Model manipulation detection | No | Partial | Yes |
Who It's For
Built for the teams responsible for AI in production.
01Product and engineering teams who have deployed AI features or agents in production.
02Security teams responsible for governing AI usage across the organisation.
03Compliance and risk functions that need AI audit trails for regulatory requirements.
04AI platform teams building internal GenAI infrastructure.
05Any organisation that has deployed LLM-powered applications and cannot answer: 'What is my AI doing right now?'
“Most enterprises deploying AI today are doing so without any visibility into what those systems are actually doing at runtime. AIDR doesn't slow your AI adoption — it makes it defensible.”
Nanda Kumar — Founder & CEO, SaaviGenAI
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