Trustwise Launches the First Trust Layer for Agentic & Generative AI    -    LEARN MORE
Trustwise Launches the First Trust Layer for Agentic & Generative AI    -    LEARN MORE
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Monthly Archives: September 2025

Bridging the Trust Gap: Why Enterprise AI Needs Trust at the Decision Level

Posted on September 9, 2025 - 5:15 am by
Trust Gap blog

By Manoj Saxena, CEO and founder of Trustwise

As AI systems evolve from passive tools to active decision makers, we’re witnessing a shift that traditional security models weren’t built to address. Enterprise adoption of autonomous agents is exploding, with Gartner predicting that 33% of enterprise software applications will include agentic AI, and at least 15% of day-to-day work decisions will be made autonomously through AI agents by the end of 2027. But agents can act unpredictably, overspend, leak data, and go off-policy, introducing a new class of risk and creating what we call the AI Trust Gap.

This isn’t a tooling problem. It’s a trust problem. And trust isn’t just about security. Trust entails proving that agents operate safely, align with internal and external compliance, and are optimized for cost and carbon efficiency.

Anyone can build agents from code writers to copilots, but few can ensure they operate safely and efficiently. 

CISOs and IT leaders are grappling with escalating safety and security concerns including AI hallucinations, data leakage risks, and uncontrolled agent behaviors that traditional security can’t govern in real-time. Existing tools like observability and prompt filtering can’t stop prompt injections, toolchain exploits, or message compromise that hijack agent behavior. Agentic systems make thousands of micro-decisions per second, each one potentially impacting safety, security, compliance, brand reputation, and operational efficiency. 

The problem is, you can’t put a firewall around a decision.

Enter Trustwise: The Trust Layer for Agentic AI 

Without runtime enforcement, over 80% of enterprise AI projects stall due to unreliability, inefficiency, or governance failures. Trustwise turns agent behavior into a governed, provable, and optimized asset so enterprises can scale AI with confidence.

Harmony AI, our flagship product, delivers a Trust Management System. The platform’s Control Tower gives customers visibility and management of their agentic and generative AI deployments. Its innovative multi-shield architecture (Prompt Shield, Compliance Shield, Brand Shield, Cost Shield, Carbon Shield) transforms AI safety from reactive monitoring to proactive governance.

Harmony AI closes the Trust Gap gap by providing:

  • Safety: Ensuring reliable, compliant AI behavior by maintaining brand voice and reputation standards, preventing harmful responses, and enforcing business rules and regulatory compliance in real-time
  • Security: Protecting against AI-specific threats like prompt injection and manipulation attempts, securing multi-model communication protocols, and providing centralized security orchestration across diverse AI deployments
  • Efficiency: Optimizing performance while maintaining governance through intelligent cost optimization, carbon impact minimization, and performance optimization that maintains response times

Achieving “Trust as Code” With Harmony AI

Harmony AI embeds “trust as code” directly into AI systems, delivering comprehensive AI governance at runtime through our modular Trust Management System. This inside-out architecture ensures your AI systems are inherently safe, aligned, and ready for scale.

Unlike traditional perimeter-based security approaches that assume you can control what enters your environment, Trustwise operates inside the AI decision loop.

When an AI agent makes decisions in milliseconds, traditional security monitoring can’t intervene. Trustwise shields work in concert to create a comprehensive trust layer that thinks as fast as your AI agents, intercepting threats before they manifest and optimizing performance before inefficiencies compound.

Trustwise stands out from traditional tools that rely solely on observability and prompt filtering. Harmony AI operates as a runtime shield that enforces trust directly in the decision loop, achieving 90-100% runtime alignment with enterprise policies while reducing AI operational costs by 83% and carbon emissions by 64%.

The Future of Trustworthy AI

Addressing AI security concerns can’t be achieved by slowing down agentic systems or limiting their capabilities. Trust must be directly embedded into the AI decision-making process. This requires a shift from reactive monitoring to proactive governance that operates inside the AI decision loop.

Trustwise transforms security from a bolt-on afterthought to a foundational layer that operates at machine speed.

We’re at a crossroads: organizations can either continue deploying autonomous agents with traditional security approaches that leave them vulnerable to the Trust Gap, or they can embrace a reality where trust is embedded directly into AI systems from the ground up. Enterprises investing in comprehensive trust infrastructure today will be the ones who unlock AI’s full potential tomorrow.

Ready to transform your unpredictable AI agents into shielded, compliant digital workers? Learn more about how Trustwise can help your organization safely scale enterprise AI deployment.

Prompt Injection Attacks Are a Wake Up Call for AI Security

Posted on September 5, 2025 - 1:32 am by
Image 2 prompt injection

By Matthew Barker, head of AI research and development

Three high-profile security incidents recently revealed how AI assistants integrated into enterprise workflows can become weapons against their users. Amazon Q, GitHub Copilot, and Google Gemini each fell victim to prompt injection exploits that demonstrate a fundamental shift in cybersecurity risks. 

These attacks represent more than isolated vulnerabilities. They expose an entirely new attack surface that circumvents conventional security measures by targeting the core functionality that makes AI assistants valuable: their capacity to understand natural language commands and execute actions autonomously.

Amazon Q: When Developer Tools Turn Destructive

In July 2025, security researchers discovered a vulnerability in Amazon’s developer extension for Visual Studio Code. An attacker had successfully infiltrated the open-source repository and embedded malicious code in the production release. The embedded instructions commanded the AI to begin a systematic data destruction process across user systems and cloud environments.

The malicious payload contained explicit directions to eliminate file systems, remove user configurations, identify AWS credentials, and leverage command-line tools to destroy cloud resources including storage buckets, compute instances, and identity management settings. AWS later acknowledged that while the attack vector was real, formatting errors prevented the destructive code from executing properly. So while the attack did not go through, its prevention was accidental, not by intentional security design. 

GitHub Copilot: Weaponizing Code Assistance

Security researchers identified a major flaw in GitHub’s AI coding assistant that enabled remote command execution through carefully crafted prompts. The vulnerability exploited Copilot’s ability to write configuration files, specifically targeting workspace settings.

Attackers could trigger “YOLO mode” by manipulating settings files to disable the need for users to confirm any configuration settings. This experimental feature, included by default in standard installations, granted the AI complete system access across multiple operating systems.

The attack relied on malicious instructions hidden within source code, documentation, or even invisible characters that developers could not see but AI systems would still process. Once activated, the compromised assistant could modify its own permissions, execute shell commands, and establish persistent access to compromised machines.

This vulnerability enabled the creation of AI-controlled networks of compromised developer workstations. More troubling was the potential for threats that embedded themselves in code repositories and propagated as developers downloaded and worked with compromised projects.

Google Gemini: Bridging Digital and Physical Worlds

Researchers at Israeli universities demonstrated the first documented case of an AI hack causing real-world physical consequences. Their proof-of-concept attack successfully controlled smart home devices through Google’s Gemini AI.

The attack began with seemingly innocent calendar invitations containing hidden instructions. When users asked Gemini to review their upcoming schedule, these dormant commands activated, allowing researchers to control lighting, window coverings, and heating systems in a Tel Aviv apartment without the residents’ knowledge.

The calendar entries included carefully crafted prompts that instructed Gemini to assume control of smart home functions. Using a technique called delayed automatic tool activation, the researchers bypassed Google’s existing safety mechanisms across 14 different attack vectors.

Beyond home automation, the researchers showed how compromised Gemini instances could distribute unwanted links, produce inappropriate content, access private email information, and automatically initiate video conferences.

Understanding the New Threat Landscape

These incidents reveal a shift in cybersecurity. Traditional security frameworks focus on blocking unauthorized system access, but prompt injection attacks weaponize the trust relationship between users and their AI assistants.

Industry experts note that prompts are becoming executable code, creating an attack surface that traditional security tools aren’t designed to detect or prevent. The Amazon Q incident particularly highlights how AI assistants can become vectors for supply chain compromise.

The attacks are concerning because they don’t necessarily require advanced technical expertise. As researchers noted, the techniques can be developed using plain language that almost anyone can create. They exploit trusted distribution channels and can remain hidden from users while still affecting AI behavior.

Many current prompt security tools treat prompts like static text streams. They filter words, blocking jailbreaks or toxic terms, but remain blind to deeper exploits such as logic hijacks, memory contamination, or unsafe tool use. As a result, they often fail against the kinds of attacks described above against Amazon Q, GitHub Copilot, and Google Gemini.

Building Effective Defenses

As organizations expand their reliance on AI-powered tools for development, operations, and business processes, implementing robust protections against prompt injection is essential. This requires treating AI prompts with the same scrutiny applied to executable code, establishing comprehensive access controls for AI agents, and deploying real-time monitoring systems for suspicious instructions.

Trustwise’s Harmony AI is a Trust Management System that continuously monitors AI interactions and identifies potentially harmful prompts before execution. Harmony AI enforces safety and efficiency at runtime with multiple modular Shields that align agents to regulatory, brand, and business requirements while containing unsafe or emergent behaviors such as hallucinations or self-preservation. With the Prompt Shield, the Amazon Q supply chain attack could have been intercepted, and the malicious instructions would have been blocked before reaching production environments.

AI’s potential benefits still remain, but these incidents serve as warnings that security frameworks must evolve alongside technological advancement. Organizations need to be prepared to defend themselves against prompt injection attacks – not if they happen but when they happen. 

Ready to explore scaling AI with confidence? Learn more about Trustwise Harmony AI’s six-shield architecture and the Control Tower to transform vulnerable AI agents into hardened, security-first systems with proactive governance.