AI Security Guide Protecting AI Systems, LLMs and Enterprise Infrastructure

The rapid adoption of large language models (LLMs), AI agents, and generative AI tools has created a new category of security risk. According to the IBM 2024 Cost of a Data Breach Report, the average breach now costs $4.88 million, and AI-related incidents are among the fastest-growing vectors. Organizations deploying AI without dedicated security measures face risks that go beyond conventional IT threats.

Prompt Injection

Prompt injection is the most exploited vulnerability in production LLM deployments. Attackers craft inputs that override a model's system instructions, causing it to leak confidential data, execute unauthorized actions, or bypass safety guardrails. Direct injection targets the prompt itself; indirect injection embeds malicious instructions in external data sources the model retrieves (such as emails, web pages, or documents in a RAG pipeline). Defending against prompt injection requires input validation, instruction anchoring, output filtering, and continuous red teaming.

Data Poisoning

Training data poisoning corrupts the datasets used to train or fine-tune AI models. Adversaries inject manipulated data that causes the model to produce biased, incorrect, or attacker-controlled outputs under specific conditions. This attack is especially dangerous because it happens during training, meaning the compromised behavior is baked into the model weights. Organizations must validate data provenance, scan training corpora for anomalies, and use data integrity verification throughout the ML pipeline.

Model Theft and Extraction

Proprietary AI models represent significant intellectual property. Model extraction attacks use carefully crafted API queries to reverse-engineer a model's weights, architecture, or decision boundaries. An attacker who steals your model can replicate your capabilities, find exploitable weaknesses, or undercut your competitive advantage. Defenses include rate limiting API access, monitoring for extraction patterns, watermarking model outputs, and restricting endpoint exposure.

Adversarial Attacks

Adversarial examples are inputs deliberately crafted to cause misclassification or incorrect outputs. In image recognition, imperceptible pixel changes can make a model misidentify an object. In NLP, small text perturbations can flip sentiment analysis or bypass content filters. These attacks target the mathematical foundations of how neural networks process information. Penetration testing that includes adversarial AI scenarios is now essential for any organization running production ML systems.

Shadow AI

Shadow AI refers to unauthorized AI tools adopted by employees without IT or security team approval. Staff may paste sensitive company data into public ChatGPT, use unapproved AI coding assistants, or deploy fine-tuned models on personal cloud accounts. According to Gartner, by 2027 more than 75% of employees will use generative AI outside of IT visibility. Shadow AI creates unmonitored data exposure, compliance violations, and an unknown attack surface. Governance policies, approved tool lists, and DLP monitoring are critical countermeasures.

AI-Powered Social Engineering

Threat actors now use AI to generate highly convincing phishing emails, deepfake voice calls, and synthetic video. AI-generated phishing campaigns can produce personalized messages at 10 times the speed of manual campaigns. Deepfake voice cloning has been used to impersonate executives and authorize fraudulent wire transfers. Defending against AI-powered social engineering requires updated security awareness training, voice verification protocols, and AI-driven email analysis tools.

NIST AI Risk Management Framework (AI RMF)

Released by the National Institute of Standards and Technology, the AI RMF provides a voluntary framework for managing risks throughout the AI lifecycle. It defines four core functions: Govern, Map, Measure, and Manage. Organizations use AI RMF to establish AI governance structures, identify risk categories, measure trustworthiness, and implement controls. PTG helps clients map AI RMF to their existing CMMC and NIST 800-171 compliance programs, creating unified governance rather than siloed frameworks.

OWASP Top 10 for LLM Applications

The OWASP Foundation's Top 10 for LLM Applications is the de facto standard for understanding security risks specific to large language models. It covers prompt injection (LLM01), insecure output handling (LLM02), training data poisoning (LLM03), model denial of service (LLM04), supply chain vulnerabilities (LLM05), sensitive information disclosure (LLM06), insecure plugin design (LLM07), excessive agency (LLM08), overreliance (LLM09), and model theft (LLM10). We use this framework as the starting point for every AI cybersecurity assessment we conduct.

EU AI Act Compliance

The EU AI Act classifies AI systems into risk tiers (unacceptable, high, limited, minimal) and imposes mandatory requirements on high-risk deployments. Even if your organization operates primarily in the US, you may need EU AI Act compliance if you serve European customers or process their data. Requirements include risk assessments, transparency obligations, human oversight mechanisms, and technical documentation. Organizations already compliant with CMMC or HIPAA have a head start, as many technical controls overlap.

AI Supply Chain Security and SBOM

An AI Software Bill of Materials (AI SBOM) documents every component in your ML pipeline: base models, fine-tuning datasets, libraries, inference frameworks, and third-party plugins. Just as software SBOMs track code dependencies, AI SBOMs track model provenance. This is critical because a compromised pre-trained model or poisoned dataset from a public repository can introduce vulnerabilities you never wrote. PTG's AI security assessments include AI supply chain audits that map your complete model dependency tree and flag components with known vulnerabilities or unverified provenance.

Input Validation and Prompt Guardrails

Every user input reaching an LLM must pass through validation layers before processing. This includes length limits, character filtering, known-malicious pattern detection, and semantic analysis for instruction-override attempts. System prompts should use instruction anchoring techniques that resist override, and sensitive operations should require multi-step confirmation rather than single-prompt execution. PTG deploys layered input filtering on our own AI infrastructure and brings the same approach to client engagements.

Output Filtering and Sanitization

LLM outputs should never be trusted as safe. Before any model output reaches a downstream system, it must be sanitized for code injection, HTML/script injection, PII leakage, and confidential data exposure. Structured output validation (ensuring responses match expected schemas) prevents LLMs from being weaponized as injection vectors. For RAG systems, output filtering also verifies that retrieved documents were authorized for the requesting user's access level.

Model Access Controls

Production AI deployments need the same access control rigor as any critical system. This means API key rotation, role-based access to model endpoints, network segmentation between inference servers and internal systems, audit logging of all model interactions, and rate limiting to prevent extraction attacks. For organizations running self-hosted models (using ollama, vLLM, or llama.cpp), securing the inference endpoint is as important as securing the model weights themselves.

AI Red Teaming

Traditional penetration testing does not cover AI-specific attack vectors. AI red teaming involves attempting prompt injection, data extraction, jailbreaking, adversarial input attacks, and privilege escalation through AI agent tools. Red team findings feed directly into guardrail updates, training data improvements, and policy changes. PTG conducts AI red teaming as part of our AI cybersecurity solutions engagement.

Petronella Technology Group runs its own GPU inference fleet with NVIDIA hardware, deploys open-source LLMs using ollama, vLLM, and llama.cpp, and builds production RAG systems for clients. This means when we assess your AI security posture, we understand the real attack surface because we defend it ourselves every day.

AI Security Assessment

Our assessment covers your complete AI stack: model inventory, data pipeline security, API endpoint hardening, access controls, output filtering, governance policies, and compliance alignment (NIST AI RMF, OWASP LLM Top 10, EU AI Act). You receive a prioritized remediation roadmap with estimated effort and risk scores for every finding. Led by Craig Petronella (CMMC-RP, CCNA, CWNE, DFE #604180) and a fully CMMC-RP certified team.

AI Security Implementation

After assessment, we implement the controls: prompt injection defenses, output sanitization pipelines, model access controls, network segmentation for inference endpoints, AI SBOM documentation, shadow AI governance policies, and continuous monitoring dashboards. For organizations with CMMC or HIPAA obligations, we map AI controls directly to existing compliance requirements.

Ongoing AI Security Monitoring

AI threats evolve as fast as the models themselves. Our AI cybersecurity solutions include ongoing monitoring: adversarial input detection, model drift analysis, access anomaly alerts, and quarterly AI red team exercises. We update guardrails as new attack techniques emerge and provide monthly security posture reports.