All AI Security Needs in a Single AI-Powered Platform

Attackers embed hidden instructions within seemingly normal prompts that override the AI's safety guidelines. The attack typically uses special characters, role-playing scenarios, or instruction hierarchies to confuse the model. The AI then follows the malicious instructions instead of its intended behavior, potentially revealing confidential data or performing unauthorized actions.

Jailbreaking exploits use role-playing scenarios, hypothetical framings, or encoded instructions to trick AI into ignoring safety protocols. Common techniques include the DAN (Do Anything Now) method, character roleplay exploits, and gradual context shifting. These attacks progressively desensitize the AI to restrictions until it complies with harmful requests.

Attackers use targeted prompts to make AI systems reveal information they've processed or learned. Techniques include asking for summaries of previous conversations, requesting data in specific formats, or exploiting the model's tendency to complete patterns. The AI inadvertently becomes a channel for data theft without traditional network-based detection.

Attackers leverage AI to create personalized phishing campaigns, generate convincing impersonation scripts, or build trust through extended conversations. The AI analyzes target information to craft contextually relevant attacks. These attacks exploit human trust in AI systems and the convincing nature of AI-generated content.

Attackers send carefully crafted queries and analyze response patterns to infer model characteristics. By observing how the model responds to edge cases and specific inputs, they can reconstruct training data, identify model weaknesses, or steal intellectual property. This is particularly dangerous for models trained on sensitive or proprietary data.

Attackers add carefully calculated perturbations to inputs that exploit model vulnerabilities. These modifications are often imperceptible to humans but cause the AI to misinterpret the data completely. Techniques include gradient-based attacks, black-box attacks, and transferable adversarial examples that work across different models.

AI systems may inadvertently violate compliance by processing personal data without proper consent, failing to anonymize sensitive information, or retaining data beyond legal limits. Cross-border data transfers through AI APIs, inadequate audit trails, and lack of explainability in AI decisions also create compliance risks.

Attackers exploit AI's training on proprietary data to extract valuable IP. Through targeted prompts, they can retrieve code snippets, formulas, business strategies, or creative content. The AI's ability to synthesize and recombine information makes it a potential channel for IP leakage that bypasses traditional DLP systems.

Employees use personal accounts or unapproved AI services for work tasks, bypassing corporate security controls. This includes using ChatGPT, Claude, or other AI tools with company data. Shadow AI creates unmonitored data flows, compliance violations, and security vulnerabilities outside IT visibility.

Attackers bypass content filters through creative prompting, context manipulation, or gradual escalation techniques. They exploit AI's training on diverse internet data to generate problematic content. This abuse can spread misinformation, harm individuals, or damage organizational reputation.

Attackers study AI behavior patterns to identify exploitable business logic. They then craft inputs that technically follow rules but achieve unintended outcomes. Examples include manipulating dynamic pricing, gaming recommendation algorithms, or exploiting automated approval systems through pattern recognition.

Attackers provide false historical context, claim previous permissions were granted, or reference non-existent prior conversations. They exploit the AI's context window limitations and tendency to trust user-provided context. This can lead to bypassed security measures or incorrect AI behaviors based on fabricated history.