5 Best Practices to Secure AI Systems (and Why “Just Add MFA” Isn’t a Strategy)

Artificial intelligence has a talent for making us feel simultaneously futuristic and slightly irresponsible. You can deploy an AI assistant that drafts customer emails in six languages, summarizes incident reports, and writes Terraform that mostly works on the first try. And then—because we can’t have nice things—it also opens up new ways to leak data, trigger unsafe actions, or quietly corrupt decision-making at scale.

That tension is the core message behind the sponsored piece “5 best practices to secure AI systems” published by AI News (TechForge Publications) on April 2, 2026, credited to WebFX. The article outlines five foundational security practices—governance, defenses against model-specific threats, visibility, monitoring, and incident response—and then points readers at a few well-known security vendors. citeturn3view0

This article is my expanded, journalist-style deep dive using that RSS item as a launching pad. I’ll keep the spirit—practical security guidance—but add the parts you need in real life: threat models, frameworks, implementation details, team responsibilities, and the uncomfortable truth that AI security is mostly “regular security,” plus a few brand-new ways to get owned.

Why AI security feels different (even when it isn’t)

Traditional software is usually deterministic: if input X happens, code path Y runs. AI systems—especially LLM-based apps—are probabilistic and context-driven. That means:

Inputs can be instructions (prompt injection), not just data.
Outputs can be actions (agents calling tools, APIs, or workflows).
Behavior changes over time (model updates, new retrieval sources, drift).
Security boundaries get fuzzy (what’s “data,” what’s “code,” what’s “policy,” what’s “prompt”? Sometimes all four.)

Industry bodies have responded by creating AI-specific taxonomies and frameworks. A big one is the OWASP Top 10 for Large Language Model Applications, which explicitly lists Prompt Injection as LLM01, underscoring how central this risk is for modern LLM apps. citeturn4search0turn4search3

On the governance side, we now have “management system” standards like ISO/IEC 42001:2023, positioned as an AI management system standard to help organizations define policies, objectives, and processes for responsible AI development and use. citeturn5search0

And on the U.S. risk management side, NIST’s AI Risk Management Framework (AI RMF 1.0) was released on January 26, 2023 as voluntary guidance for organizations to manage AI risks. citeturn5search2turn5search1

Those frameworks won’t magically secure your models. But they do provide a common language—useful when you’re explaining to leadership why “we disabled copy/paste in the chat window” does not qualify as a control.

Best practice #1: Enforce strict access and data governance (a.k.a. the least exciting, most effective control)

The AI News / WebFX piece leads with access control and data governance—correctly. AI systems are hungry. They ingest data, logs, documents, embeddings, model weights, prompt templates, and tool credentials. If you don’t govern access, the rest of your controls become expensive theater. citeturn3view0

What “access control” means in an AI stack

In a modern LLM application, “access” isn’t just user login. It includes:

Who can query the model (end users, internal staff, service accounts).
Who can change prompts (system prompts, routing prompts, tool instructions).
Who can change retrieval sources (RAG document stores, connectors, indexes).
Who can fine-tune or retrain (training pipelines, MLOps roles).
Who can view logs (prompt logs and output logs often contain sensitive data).
Who can ship model artifacts (weights, adapters, quantized variants).

Role-based access control (RBAC) is a good baseline. In practice, mature organizations blend RBAC with least-privilege service accounts, short-lived credentials, and approvals for high-impact changes (like deploying a new prompt or enabling a new tool for an agent).

Data governance: define the “AI data lifecycle,” not just “data at rest”

The sponsored article calls out encryption at rest and in transit—also correct, and still routinely missed. citeturn3view0

But AI introduces more “data states” than a classic web app. Consider adding explicit governance for:

Training data (source legitimacy, licensing, privacy, provenance).
Fine-tuning data (often highly sensitive and domain-specific).
Inference data (user prompts, uploaded documents, chat histories).
Derived data (embeddings, summaries, synthetic data, cached completions).
Observability data (traces, evaluation artifacts, red-team transcripts).

U.S. government security agencies have explicitly highlighted how critical it is to secure the data used to train and operate AI. In May 2025, CISA announced a joint Cybersecurity Information Sheet on AI Data Security with NSA, FBI, and international partners, emphasizing best practices for securing data used to train and operate AI systems. citeturn4search2

Quick wins you can implement this quarter

Inventory the AI assets: models, endpoints, datasets, vector stores, prompt repos, evaluation datasets.
Separate duties: people who can change prompts shouldn’t automatically be able to change tool permissions or data connectors.
Encrypt everything that matters: not just the database—also object storage, model artifact registries, and logs.
Log access to AI assets: especially retrieval sources and prompt templates.

Best practice #2: Defend against model-specific threats (welcome to the prompt layer)

The WebFX article specifically calls out prompt injection as the top-ranked vulnerability in OWASP’s LLM Top 10, and recommends AI-specific firewalls/input validation plus adversarial testing (red teaming). citeturn3view0turn4search0

Prompt injection: the new SQL injection (but with vibes)

OWASP defines prompt injection in the context of LLM apps as a vulnerability where user prompts alter the model’s behavior or output in unintended ways. citeturn4search3

In a simple chatbot, prompt injection might produce policy-violating text. In an agent (LLM + tools), prompt injection can trigger actions: sending emails, modifying tickets, querying internal systems, or exfiltrating data through “helpful” summaries. In a RAG system, injection can also be indirect—malicious instructions embedded inside documents that the model retrieves and treats as context.

Defense-in-depth for LLM apps (practical version)

You’re not going to “solve” prompt injection with a single regex. Instead, stack controls:

Input handling: normalize, classify, and route prompts; apply allow/deny rules for high-risk intents.
System prompt hygiene: keep system prompts short, versioned, reviewed, and tested like code.
Tool gating: require explicit policy checks before a tool runs (especially write actions).
Output constraints: enforce structured outputs (schemas) for tool calls; reject anything malformed.
Context boundary rules: treat retrieved documents as untrusted; label sources; prevent “document text” from masquerading as instructions.
Secrets discipline: never put secrets in prompts; assume prompts will leak into logs.

Adversarial testing and red teaming: make it continuous, not ceremonial

The sponsored piece recommends regular adversarial testing and red team exercises to simulate attacks like data poisoning and model inversion. citeturn3view0

To scale that idea beyond a once-a-year exercise, many vendors and labs are turning red teaming into an ongoing practice. OpenAI, for example, created the OpenAI Red Teaming Network to involve external experts in broader risk assessment and mitigation efforts. citeturn6search4

OpenAI has also published guidance on combining people and automation for red teaming, describing how red team findings can be turned into repeatable evaluations for future model updates. citeturn6search8

If you’re building enterprise AI, steal that playbook: turn your best red-team attacks into automated tests that run in CI/CD (or at least before each prompt/model update).

Threat modeling with AI-specific maps

Classic threat modeling methods still help, but AI security benefits from AI-specific catalogs:

MITRE ATLAS (Adversarial Threat Landscape for AI Systems) organizes and describes AI-focused attack pathways; MITRE has emphasized updates to ATLAS to address generative AI security risks and its role as a common language for defenders. citeturn5search3turn5search9
OWASP LLM Top 10 provides a baseline vulnerability taxonomy for LLM applications. citeturn4search0

Best practice #3: Maintain detailed ecosystem visibility (because AI systems sprawl fast)

The third practice in the WebFX article is ecosystem visibility: AI environments span on-prem networks, cloud infrastructure, email systems, endpoints, and more—and siloed security data creates blind spots. citeturn3view0

This is the part where many organizations discover that their “AI project” is actually:

an LLM API endpoint,
plus a vector database,
plus a file connector to SharePoint/Google Drive,
plus an internal tool API,
plus a logging pipeline,
plus a handful of service accounts that will definitely have more permissions than they should.

Visibility checklist (what you should be able to answer in 30 seconds)

Which AI endpoints exist, and who can access them?
Which prompts (system, developer, routing) are in production right now?
Which tools can agents call, with what scopes?
Which retrieval sources feed the system, and what’s the trust level of each source?
Where are prompts and outputs logged, and who can read those logs?
What model version is running, and when was it updated?

Framework perspective: secure the whole system, not just the model

Several modern AI security frameworks emphasize breaking the system into components. Google’s Secure AI Framework (SAIF) divides its map into four component areas: Data, Infrastructure, Model, and Application. citeturn6search0

That component framing is valuable because it prevents a common mistake: teams over-invest in “model safety” while under-investing in basic infrastructure security. Attackers tend to enjoy that imbalance.

Unifying telemetry: the “single pane of glass” cliché, but useful

Practically, detailed visibility means unifying logs/traces from:

Identity: auth events, token issuance, permission changes.
API gateways: rate limits, anomalies, abuse patterns.
RAG retrieval: which docs were retrieved, from where, with which query.
Tool calls: what tools were invoked and with what parameters.
Model layer: prompt templates, context windows, safety filter decisions.
Infrastructure: container runtime, GPU nodes, network flows.

Yes, it’s a lot. No, the answer is not “we turned on debug logging.” You need a deliberate logging policy that balances security, privacy, and cost—especially because LLM logs can become a data leak in their own right.

Best practice #4: Adopt a consistent monitoring process (because models drift and attackers don’t sleep)

The fourth practice in the original piece is continuous monitoring, focusing on baselining behavior and alerting on deviations: unexpected outputs, API pattern changes, or privileged accounts accessing unusual data. citeturn3view0

What you should monitor in AI systems (beyond uptime)

AI monitoring needs to cover both security and reliability. Security monitoring should include:

Prompt and tool abuse patterns: repeated jailbreak attempts, suspicious instruction patterns, unusual tool sequences.
Data exfil signals: large retrieval volumes, repeated queries for sensitive terms, export-like behavior.
Policy bypass indicators: sudden drop in refusals, spikes in restricted topics, or tool calls happening without expected checks.
RAG poisoning indicators: newly ingested documents causing abnormal output shifts or instructions appearing in retrieved context.
Cost anomalies: “unbounded consumption” isn’t just a FinOps problem; it can be an abuse signal (and OWASP calls out resource issues as a class of risk in LLM apps). citeturn4search0

Behavior baselines: don’t just alert on 500 errors

A mature monitoring practice defines “normal” for:

Tokens per request and per user,
Tool-call rate,
Top intents/categories,
Retrieval hit rates and source distribution,
Safety filter activations,
Latency distributions by model/version.

Then you alert on deviation. Not because every deviation is malicious, but because deviations are where both attacks and bad deploys live.

Monitoring must survive model updates

AI systems change frequently. Monitoring that relies on brittle signatures or static assumptions will break the moment you update a prompt template or swap model versions. That’s why the best monitoring programs pair:

traditional signals (auth anomalies, network behavior, endpoint events), and
AI-specific signals (prompt abuse, tool misuse, retrieval anomalies).

If your SOC can’t see the AI signals, your AI app has effectively created its own private internet. That’s historically not where good things happen.

Best practice #5: Develop a clear incident response plan (AI IR is not just “restore from backup”)

Finally, the WebFX article stresses incident response planning—containment, investigation, eradication, recovery—and notes that AI incidents can require retraining models, auditing outputs, and reviewing logs for what the system produced while compromised. citeturn3view0

Why AI incident response needs its own runbooks

In classic IR, you often focus on endpoints, accounts, and data stores. In AI IR, you also need to answer:

Was the model manipulated? (prompt injection, jailbreaks, unsafe tool use)
Was the data corrupted? (poisoned training data, poisoned retrieval corpus)
Was sensitive data leaked? (prompts/logs, retrieval outputs, model memorization concerns)
Was the model stolen? (model extraction, API scraping, artifact theft)

Some incidents will be “just” application abuse. Others can compromise integrity in subtle ways—like causing the model to provide consistently wrong answers for a specific customer segment, or to quietly recommend an attacker-controlled vendor in procurement workflows. That’s not science fiction; it’s the same principle as search engine poisoning and ad fraud, now wearing an LLM hoodie.

AI-specific containment actions

Disable tool actions (switch to read-only mode) while keeping chat available.
Freeze prompt versions and lock changes until review is complete.
Quarantine new RAG ingests (stop indexing new documents; isolate suspicious sources).
Rotate credentials used by agents/tools (service accounts, API keys).
Enable higher-friction controls temporarily (human approvals, stricter policies, more aggressive filtering).

Recovery might include retraining or re-indexing (and communicating it)

If a retrieval store is poisoned, you may need to rebuild the index from known-good sources. If training data or fine-tuning data is compromised, retraining may be required—along with a postmortem documenting how integrity was restored.

Also: prepare your communications team. “We rotated keys” is one thing. “Our AI assistant may have provided incorrect guidance between March 12 and March 18” is a different kind of incident message. You want that drafted before you need it.

Putting it together: a secure-by-design AI lifecycle

Security programs fail when best practices live as bullets in a slide deck instead of gates in a lifecycle. The easiest way to operationalize these five practices is to align them to phases:

Plan: define governance, asset inventory, risk appetite, acceptable use.
Build: secure coding, prompt versioning, dependency control, secrets discipline.
Validate: automated evals, red teaming, adversarial tests mapped to OWASP/ATLAS.
Deploy: hardened infrastructure, least privilege, logging and telemetry, runtime guardrails.
Operate: continuous monitoring, drift detection, incident response drills.

NIST’s AI RMF 1.0 is often used as a broader umbrella for organizing AI risk work, even though it’s not a prescriptive cybersecurity standard. citeturn5search2

For security teams specifically, NIST has also been working on AI-focused cybersecurity profiling under the CSF model; NIST’s community profile work on a “Cyber AI Profile” highlights outcomes such as securing AI system components and conducting/thwarting AI-enabled cyber attacks. citeturn5search11turn5search20

Vendor tools vs. vendor outcomes (a polite reality check)

The AI News sponsored post includes a “Top 3 providers” section naming vendors like Darktrace, Vectra AI, and CrowdStrike. citeturn3view0

Those products may be relevant depending on your environment, but AI security is not a shopping list. Tools help when you already know:

what you’re securing (inventory),
who owns which controls (RACI),
what your risks are (threat model), and
what “good” looks like (policy + measurable outcomes).

If you buy an “AI firewall” without defining tool permissions and data connectors, you’ll end up with a very secure front door and a suspiciously open garage. Attackers love garages.

Case-study patterns: what goes wrong most often

Without repeating anyone’s marketing claims, here are recurring patterns I see across enterprise AI rollouts:

1) The prompt repo is treated like a wiki

Prompts get edited directly in production UIs. No reviews. No versioning. No tests. Then an internal user pastes a “helpful” instruction from a blog post, and suddenly the agent starts dumping internal policy docs into customer-facing chats.

Fix: Treat prompts like code. Version them, review them, test them, and restrict who can modify them.

2) RAG connectors become the new data breach vector

Teams connect the assistant to “all company docs” for maximum helpfulness. That typically means overly broad permissions. The model then becomes a search engine that can be coaxed into revealing HR data, legal drafts, credentials in runbooks, or customer contracts.

Fix: Least privilege retrieval, document classification, and explicit allowlists of sources for each assistant persona/use case.

3) Agents get write access too early

It’s tempting to let an AI agent “just create the Jira ticket” or “just send the email.” That’s fine—until the agent is manipulated into sending the wrong email to the wrong person with the wrong attachment.

Fix: Start read-only. Add human approvals for writes. Use strong policy enforcement at tool boundaries.

4) Logs become a shadow data lake of secrets

Prompt/output logs are incredibly useful for debugging. They are also an excellent way to store sensitive data in places your data governance program never anticipated.

Fix: Minimize logging, redact sensitive fields, enforce retention limits, and restrict log access.

A pragmatic implementation roadmap (90 days)

If you’re a CISO, security architect, or the unlucky person who got assigned “AI governance” because you once mentioned NIST in a meeting, here’s a realistic 90-day plan aligned to the five practices.

Days 1–30: establish governance + inventory

Create an AI asset inventory (models, endpoints, datasets, vector stores, prompts, tools).
Define roles and approvals: who can deploy a model, change a prompt, add a connector.
Set data classifications and prohibited data categories for AI inputs and logs.
Turn on encryption and key management everywhere AI assets live.

Days 31–60: threat model + test harness

Run an AI threat modeling workshop using OWASP LLM Top 10 and MITRE ATLAS as references. citeturn4search0turn5search3
Build a red-team prompt suite (jailbreaks, indirect injection, tool abuse attempts).
Create automated evaluations for the most likely failure modes.
Define tool boundary checks (schema validation, allowlists, rate limits).

Days 61–90: monitoring + incident response

Implement unified telemetry across identity, API gateway, retrieval, tool calls, model layer.
Baseline normal usage and alert on anomalies (behavioral, not just error codes).
Write AI-specific IR runbooks (RAG poisoning, prompt injection abuse, tool compromise).
Run a tabletop exercise with engineering, security, legal, and comms.

Conclusion: secure AI is a program, not a product

The original AI News article gets the fundamentals right: access and governance, model-specific defenses, ecosystem visibility, continuous monitoring, and incident response planning. citeturn3view0

My take is less poetic and more operational: every AI system is a distributed system with a probabilistic brain and a growing collection of connectors, tools, and data paths. If you secure it like a single “model endpoint,” you’ll miss where the real risk lives.

Do the boring stuff (RBAC, encryption, logging, IR), then do the new stuff (prompt-layer threat modeling, tool gating, adversarial testing). Your future self—and your legal team—will appreciate it.

Sources

Bas Dorland, Technology Journalist & Founder of dorland.org