AI has moved to the mainstream across enterprises. Machine learning and large language models now run many of the systems that handle decisions, automate tasks, and personalize customer experiences. Yet since these tools are an integral part of daily business, they are also increasingly targeted. Attackers are looking beyond traditional infrastructure and aiming straight at AI itself – especially the LLMs, the data, and the supply chains that support them.

AI adoption comes with hidden risks and new threat vectors that require structured assessments and adversarial simulations to uncover weaknesses before attackers do.

How AI Changes the Attack Surface

AI is changing where risk lives. Every model, dataset, and integration creates openings that traditional security tools cannot see. And exposure is constantly growing as AI adoption spreads across daily workflows. One in twenty enterprise users now accesses generative AI applications, and nearly six in ten employees use unapproved AI tools at work – most of them sharing sensitive data. Data integrity is also under pressure;      only a few hundred poisoned documents can compromise a large language model,      even if they comprise just a tiny fraction of its training set.

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Each AI-related connection, plugin, or external data source can introduce unseen risk. These changes have created three primary AI-oriented threat vectors that define the modern attack surface. Each one targets a different layer of the ecosystem – how models think, how data is built, and how code and components move through the supply chain.

Prompt Injection – The Manipulated Mind of AI

Prompt injection attacks manipulate how AI systems interpret instructions and inputs. Attackers design malicious language that overrides safeguards, which can cause models to expose sensitive data or take unauthorized actions.. Since these attacks exploit the way LLMs process system and user prompts together in one context, prompt-injection attacks can hide malicious instructions among otherwise normal text. The The OWASP Top 10 risks for GenAi lists prompt injection as the most critical risk for large language model applications. Tests across dozens of commercial and open-source systems show that more than half of simulated injections succeed.

Traditional filters and firewalls cannot separate safe from unsafe instructions once they merge in a single query. Effective defense depends on layered validation, strict access control, and continuous red-team testing to reveal vulnerable pathways.

Data Poisoning – When the Source Becomes the Threat

In data poisoning attacks, attackers insert false or misleading samples into training datasets, changing how a model behaves after deployment. Even small amounts of corrupted data can distort results, alter classifications, or create hidden backdoors. Poisoned data can, for example, teach a model to treat threats as safe or include triggers that cause unexpected actions in real use.

Modern large-language models trained on open datasets could be compromised with only a few hundred malicious examples;      according to one report, even minimal contamination can skew a model’s behaviour.      Because poisoned data blends in with legitimate inputs, it can bypass typical data-quality checks.

Supply Chain Vulnerabilities – Inherited Risk in Every Dependency

AI systems run on a mix of open-source libraries, pretrained models, APIs, and outside data services. Every layer adds convenience – and risk. A single unverified download or weak integration can give attackers a path straight into production. Most modern software already carries exposure: open-source components appear in nearly every codebase, and 86% percent contain known vulnerabilities. When these dependencies sit inside automated pipelines, one hidden flaw can move through multiple applications before anyone notices.

Traditional supply chain controls can’t keep up and      static scans miss tampered code and backdoored models that slip through normal reviews. Teams need an AI-specific software BoM, isolated testing for third-party components, and regular threat simulations to stay ahead.

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From Compliance to Confidence

Traditional security frameworks weren’t built for the way AI works. Standards such as ISO 27001 or SOC 2 focus on systems, storage, and access – not on how models learn, adapt, or make decisions;      that leaves real gaps. Risks like model drift, poisoned data, and exposed APIs fall outside their scope. Compliance can check the boxes, but it doesn’t prove that defenses hold up in the real world.

AI risk assessments ,     like those from TeKnowledge,     fill that gap with a clearer view of how systems behave in practice. They show how data flows, where models connect, and how risk moves through the organization. Simulations then bring that picture to life. Red-team testing and continuous validation reveal how defenses respond under pressure and help teams track real progress instead of assumptions.

The Bottom Line – From Awareness to Assurance

AI has changed what it means to be secure. The attack surface now resides inside systems that learn, adapt, and make decisions for the business.

Every organization adopting AI faces the same choice: react later or understand the risk now. Those that take action early build trust, stability, and lasting confidence in how their systems perform under pressure. Because the strongest safeguard against AI-driven threats is an AI-aware defense.

Start your AI readiness journey today with a structured risk assessment and simulation program that turns awareness into action.

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Large language models (LLMs) are already deep inside enterprise workflows, yet most organizations are still not prepared to secure them.

A recent study found that nearly 40% of firms lack the basic data security controls like encryption and tokenization that are needed to safeguard AI adoption. A separate analysis showed that nearly 84% of enterprise data shared with AI tools is going into platforms classified as critical or high risk – meaning sensitive information is leaving the enterprise and landing in applications with limited safeguards or oversight. What’s more, 62% of organizations deploying AI have already incorporated an AI package containing at least one known vulnerability.

This paints a worrisome picture. Clearly, existing security frameworks are not designed for this environment. Controls built for static networks and perimeter defense cannot detect prompt injection, model drift, or information leakage through chained APIs. They can’t provide the visibility and behavioral monitoring that LLM pipelines demand.

Addressing these gaps starts with focus. Security leaders need to control access, separate workloads, and maintain continuous oversight. Without those fundamentals, LLM deployments are far more likely to expose sensitive data, trigger compliance failures, and disrupt operations. In this blog, we explore how secure-by-design deployment and hardened infrastructure provide the foundation enterprises need to adopt LLMs at scale with confidence.

Secure-by-Design LLM Deployment

Meeting the security demands of LLMs requires more than quick fixes. Security needs to be part of the design from the start. Enterprise AI deployments need enterprise-grade deployment models – which are designed from the ground up to reduce exposure, anticipate risks, and support compliance. To achieve this, enterprises need to focus on two areas: secure architecture and guardrails, and scalable resilience in operations.

• Secure architecture and guardrails

The first step is knowing where models will be (or are) running – in the cloud, on-prem, or in hybrid ecosystems – and what risks come with each. From there, make sure the infrastructure basics are done right: network segmentation, access controls, and encryption for data in motion or at rest. LLMs also need their own set of defenses – prompt filtering to block malicious queries, input validation to catch unsafe requests, and rate limiting to keep attackers from overloading systems with too many requests at once.

Enterprises also need to put guardrails in place, defining how models can be built and deployed. Infrastructure as Code templates and secured CI/CD pipelines prevent mistakes from slipping into production. Logging should capture inputs, outputs, and system changes, while Zero Trust access makes sure every user and process is verified. Runtime monitoring adds visibility once systems are live. And to prove that these safeguards actually work, adversarial Red Teaming is essential. Testing defenses against prompt injection, model inversion, and other attacks – benchmarked against the OWASP LLM Top 10 – shows where gaps remain and where controls need to be strengthened.

• Scalable resilience in operations

The second step is building resilience into daily operations. Enterprises need systems that can withstand pressure as LLM adoption grows. That starts with reducing the attack surface before deployment by finding and fixing weak points early. Endpoints and APIs should be tightly configured and isolated, and workloads need to be separated so that issues in one tenant do not impact others. This keeps incidents contained and prevents them from spreading across environments.

Operational maturity also requires security to scale reliably. Cloud-native and container best practices will ensure that every deployment uses the same hardened settings, so new workloads don’t introduce gaps. Security teams need continuous monitoring so they can see how models and data behave in real time. Logs must be detailed enough to show C-levels and regulators that controls are working.

Business Benefits of Hardened LLM Deployment

Enterprises that invest in hardening their LLM environments enjoy clear operational and business advantages, notably:

• Reduced operational and compliance risk at scale

Building controls directly into systems and processes stops problems before models go live. Tight access rules, secure data flows, and strong governance practices reduce the chances of breaches and fines. Rolling out these protections across all deployments keeps risk manageable as AI use grows.

• Hardened infrastructure and runtime protections

Resilient workloads and strong runtime safeguards protect against privilege escalation, model abuse, and cross-tenant exposure. Defenses like prompt filtering, input validation, and rate limiting contain manipulation attempts. These measures keep services running and protect sensitive data during live operations.

• Visibility into model interactions and usage behavior

Comprehensive logging and monitoring provide insight into how models behave and how data moves through systems. This visibility helps teams detect anomalies, refine controls, and build reliable audit trails. And this offers executives the oversight they need to align AI programs with business priorities.

• Secure deployment architecture for any LLM hosting model

Enterprises should apply consistent frameworks across cloud, on-prem, and hybrid setups. This keeps deployment options open while maintaining uniform security that scales reliably.

The Bottom Line

As enterprises adopt LLMs at scale, securing them requires a new playbook. Traditional security tools can’t stop AI-specific threats like prompt injection, model inversion, or API data leaks. Companies need to build security right into their LLM deployments from day one and make sure it works as AI use grows.

Hardened deployments solve this problem. They bring together secure infrastructure, real-time protection, and continuous monitoring so LLMs can run safely at enterprise scale. When you build security into the foundation, you reduce operational risk, avoid regulatory trouble, protect sensitive data, and give your security team clear visibility into what’s happening. You also get the audit trails that prove to executives and regulators that your controls actually work.

Get this right, and AI stops being something that keeps you up at night. It becomes something you can scale with confidence.

Ready to harden your LLM deployments and set the stage for AI resilience? Request an AI-Readiness Security Assessment from TeKnowledge today.

Generative AI has become central to enterprise operations. According to McKinsey, 71% of organizations regularly use generative AI in at least one business function. According to Netskope, that number is as high as 96%.

This surging uptake is changing the way enterprises work. Developers are embedding more models into workflows. Business units are adopting more AI-powered tools. And customers have come to expect AI-driven convenience.

The opportunities are clear, yet so are the risks. AI threats have expanded the attack surface in ways traditional defenses never imagined.

Most organizations are not prepared. Security teams have major talent gaps even as shadow AI spreads across departments. And governance frameworks are still catching up. Staying secure in this emerging ecosystem requires an approach built for the realities of AI – because today, being AI-ready is the same as being business-ready.

The Expanding AI Attack Surface

AI is changing organizational risk. Every new model, dataset, or API adds another potential foot in the door for attackers. Previously unknown risks like poisoned data, vulnerable dependencies in the AI supply chain, and prompt-based exploits are already in play. What’s more, employees are bringing in their own AI tools to work, creating “shadow AI” that spreads without oversight. Studies show that four out of five enterprise AI tools operate without management, and almost 40% of employees admit sharing confidential data with AI platforms without approval.

For security leaders, that means blind spots. They need clear visibility into where AI is used, who is using it, and how. Only then can teams build safeguards strong enough to stop risks from turning into incidents.

At the same time, the expertise needed to manage AI risk is hard to find. Most teams lack the skills for both cybersecurity and AI, creating gaps in monitoring, governance, and response. In fact, McKinsey found that half of organizations report that they need more AI scientists than they currently have – another gap that limits the ability to secure and govern AI systems.

Why Does Traditional Security Fall Short?

Simply put – traditional security programs were created for a different set of problems.

Distributed tools and static controls cannot keep up with the speed and complexity of AI. Risks such as model tampering, hidden data leaks, and compromised supply chain components straightforward solutions move faster than these defenses can respond. The situation is so serious that 74% of organizations reported an AI-related breach in 2024

Many organizations cannot say with certainty where their AI workloads run, which tools have been introduced without approval, or what happens if a model is breached. Protecting AI-driven operations requires a modern, integrated approach designed for today’s risks.

The TeKnowledge AI-Ready Security Suite

TeKnowledge has created a straightforward enterprise grade solution to meet the challenges of secure AI adoption. Our AI-Ready Security Suite is designed  around three core pillars – Assess, Implement, Optimize. This framework mirrors the way enterprises actually run security programs, so it is a practical, scalable path to secure AI operations which includes evaluating current risks, to implementing protective controls, and continually optimizing defenses as AI initiatives evolve.

The TeKnowledge AI-Ready Security Suite is modular. Each pillar delivers value on its own, or all three can run together as a complete program:

• Assess

The first step is to get a clear understanding of risk. During our assessment stage, we conduct penetration tests, red teaming excercises, AI-specific evaulations of models, data flows, governance, other AI-specific tools and methods to find gaps in security coverage. We also show how these gaps translate into business impact and where attackers would most likely strike. This evidence-based approach gives leaders the facts they need to set priorities, replacing assumptions with clarity and laying the foundation for all that follows.

• Implement

The implementation stage helps the organization move from insight to action. It puts the right foundations in place so AI can run securely at scale. It includes secure cloud migrations for AI workloads, compliance controls for regulatory needs, and SOC operations built for modern environments. The goal is to weave resilience into systems and processes from the start – so security supports growth rather than slowing it down.

• Optimize

Optimization ensures that protection keeps pace with adoption. With a focus on continuous monitoring, faster response, and training – the optimization process equips both systems and people to handle AI-specific threats. It also secures customer-facing platforms. Optimizing is about staying ahead as both the business and the threat landscape evolve. It ensures that security grows with AI and that resilience is a given no matter how quickly adoption scales.

Why TeKnowledge

Enterprises need a partner that understands the scale of AI adoption and the security challenges it creates. TeKnowledge delivers that focus. Our recognition as an elite Microsoft Partner reflects our proven expertise in Azure, Microsoft Sentinel, and Copilot security – capabilities that matter because AI relies on the same data, identity, and cloud platforms.

TeKnowledge combines global scale with local expertise, providing continuous protection through a follow-the-sun support model. The AI-Ready Security Suite was built for modern AI environments – bringing managed services, cloud operations, and customer experience security together in one framework. It streamlines operations, reduces risk, and strengthens resilience as AI adoption grows.

The Bottom Line

AI is already driving everyday business operations, but adoption is outpacing safeguards. Many organizations move ahead without the protections needed to secure their models, data, and customer trust. The TeKnowledge AI-Ready Security Suite closes the gap between rapid AI adoption and effective security. TeKnowledge provides the expertise and global reach enterprises need to innovate with confidence and still maintain control. Because today, being AI-ready is the same as being business-ready.

Ready to close the gap between rapid AI adoption and real security?  Contact Us