As AI shifts from experimental add-on to critical infrastructure, the fight to secure models, safeguard people, and protect data is reshaping law, business, and the threat landscape. The next few years will decide whether AI becomes a trusted backbone of modern life—or the next systemic risk we lose control of.

In late 2025, AI is no longer a shiny experiment bolted onto the side of business. It is the plumbing: powering customer interactions, underwriting loans, screening résumés, routing trucks, writing code, drafting contracts, and quietly watching everything. That ubiquity has transformed AI security, safety, and privacy from “future concerns” into board-level risks. Public companies now routinely disclose AI-related threats alongside cyber risk and regulatory exposure. Regulators are racing to keep pace, attackers are weaponizing the same models enterprises are deploying, and consumers are starting to understand that “smart” often means “watched.” The next few years will determine whether AI becomes a trusted critical infrastructure—or another brittle, insecure layer in an already fragile digital stack.

AI security, safety, and privacy are no longer tidy, separate domains. They overlap and collide in every major deployment decision: which model to use, what data to train on, who can access the system, what guardrails are in place, how outputs are checked, and how incidents are reported. The organizations that navigate this well will treat AI risk as part of their core governance, not as a side project owned by a single lab or innovation team.

Security: the AI arms race goes both ways

The most immediate shift ahead is the normalization of AI-powered cyberattacks. Phishing kits that once required human effort to craft now use generative models to produce linguistically flawless, context-aware messages in any language. Deepfake scams have already enabled multimillion-dollar fraud, and early incident data shows sharp growth in AI-assisted phishing, business email compromise, and rapidly morphing malware capable of evading traditional signatures. For defenders, this means that “average” attacks will begin to look like nation-state operations from a few years ago—highly tailored, highly scalable, and cheap.

On the defensive side, security teams are responding with their own AI. Models now sift through terabytes of logs, correlate subtle anomalies across cloud, endpoint, and network telemetry, and propose containment actions in seconds. Experts expect AI to orchestrate complex defenses and transform security operations. The result is not a clean victory for defenders but a high-speed arms race, where both sides lean on automation and learning systems to out-adapt the other. Over the next three years, the most mature organizations will move from “AI-assisted analysts” to semi-autonomous security copilots tightly bound by policy and human oversight. The key challenge will be ensuring that defenders can still explain and audit why a model made a certain call when a regulator, customer, or court asks.

Adversarial machine learning is the other front line. Attackers no longer only target the networks around a model; they target the model itself. Carefully crafted prompts can cause AI systems to ignore safety rules, exfiltrate training data, or quietly alter business logic. Poisoned training data can tilt outputs toward an attacker’s goals long before the model is deployed. Standards bodies and research institutions have now formalized taxonomies of adversarial attacks—prompt injection, model inversion, data poisoning, model extraction—that treat these not as theoretical curiosities but as structured risk categories that must be tested and mitigated. In the coming years, internal security reviews are likely to treat these AI-specific vulnerabilities the way they treat SQL injection or cross-site scripting today: as standard, testable security defects that require both controls and continuous monitoring.

Inside organizations, one of the most uncomfortable realities is shadow AI: the unsanctioned use of public chatbots, code assistants, and image tools by employees under pressure to move faster. Sensitive designs, contracts, and even unreleased source code often end up pasted into public models whose training and retention policies are opaque at best. Professional regulators in law, healthcare, and finance are beginning to draw bright lines around feeding confidential data into public AI tools and are requiring accuracy checks and disclosure when AI is used. In the near term, many enterprises will swing between outright bans and more nuanced “AI acceptable use” programs tied to data loss prevention systems that detect and block risky prompts in real time.

Supply chain risk is evolving just as quickly. Few organizations train models entirely in-house; most build on open-source weights, third-party APIs, plug-ins, and datasets assembled from multiple vendors. Security researchers are warning about backdoored models and poisoned datasets that can introduce hidden behaviors at scale. As a result, the concept of a software bill of materials is expanding into a model bill of materials: a structured record of where model weights, data, and fine-tuning came from, under what licenses, and under what security assurances. Auditable provenance for models—who touched them, who tuned them, what guardrails were applied—will become as important as code provenance is today.

To knit all this together, zero-trust principles are being re-imagined for AI. Traditional perimeter security is already insufficient for cloud environments; when you add AI agents that can autonomously call APIs, move data between services, and make decisions on behalf of users, blind trust becomes reckless. Forward-leaning cybersecurity teams are embedding AI into zero-trust architectures that rely on centralized identity and access management, continuous verification, and strong isolation between systems. In practical terms, that means giving models fine-grained, just-in-time permissions; logging every action they take; and treating a compromised model like a compromised human account—subject to containment, forensics, and regulatory reporting.

Safety: from principles to mandatory incident reporting

Security is about keeping adversaries out. Safety is about making sure systems behave acceptably even when everything is “working as designed.” The next phase of AI adoption will be defined by the maturation of AI safety from voluntary principles into concrete, enforceable obligations that shape real products.

The European Union is furthest along. Its AI Act entered into force in 2024 and begins applying in phases through 2027. Prohibitions on certain high-risk uses and obligations around AI literacy are already live, while governance rules and requirements for general-purpose AI models, including transparency around capabilities and training practices, are coming online in stages. High-risk systems—from medical devices to critical infrastructure to employment screening—have longer transition periods, but by mid-2027, any AI that can significantly affect people’s rights or safety in Europe will need documented risk assessments, post-market monitoring, and human oversight. For global companies, Europe’s approach is becoming a de facto safety baseline, even as policymakers and industry bodies argue over its impact on innovation and competitiveness.

The United States is taking a more fragmented path. At the federal level, there is still no comprehensive AI statute. Instead, a patchwork of sector-specific laws, guidance, and agency expectations has emerged. A recent executive order prioritized removing perceived regulatory barriers to American AI leadership, signaling a shift away from prescriptive federal oversight and increasing the importance of soft-law instruments. In that environment, frameworks from standards bodies and agencies have become the playbook for organizations trying to show that they are taking safety seriously. Risk management guidance now emphasizes system-level thinking, lifecycle governance, and context-dependent definitions of “harm” that include both physical and societal impacts.

In the absence of strong federal rules, states and other jurisdictions are stepping in. New AI safety laws in major economies, including large U.S. states, are beginning to require developers and deployers of powerful AI systems to publish redacted safety and security protocols, to maintain red-teaming programs, and to report serious safety incidents—such as AI-assisted cyberattacks, dangerous emergent behaviors, or high-impact misuses—within tight timelines. Unlike the EU’s model, which emphasizes disclosure to regulators, some of these new laws lean on public transparency and whistleblower protections as primary accountability mechanisms. If similar rules spread, AI will begin to resemble aviation or pharmaceuticals: industries where safety incidents are systematically logged, investigated, and used to update both internal processes and public rules.

The next few years will likely see the rise of “safety cases” for AI systems: structured, evidence-based arguments showing why a model is acceptably safe for a given context, backed by documentation, testing, and governance artifacts aligned with recognized frameworks. Internal AI safety engineers and red-team functions will move from the fringes into mainstream product development, especially for systems that affect health, finance, critical infrastructure, national security, or democratic processes. For many organizations, the hardest step will not be technical; it will be cultural—accepting that shipping an AI system now requires the same rigor and traceability that used to be reserved for only the most tightly regulated products.

Privacy: training data, surveillance, and the new consent battles

While security and safety grab headlines, privacy disputes are quietly reshaping the economics of AI. The biggest fight is over data used to train large models. Lawsuits around the world argue that using books, images, code, conversations, or biometric data without explicit consent is unlawful; model developers counter that training is a transformative, fair use of publicly available material. Early court decisions, including rulings that treat certain training uses as permissible under copyright doctrine, have been narrow and highly fact-specific. Regulators and policymakers emphasize that there is no one-size-fits-all answer, only a messy, evolving balancing act between innovation and individual rights.

Privacy complaints are not limited to copyright theories. Class actions and regulatory investigations are probing alleged violations of biometric privacy laws, failures to obtain valid consent, and discriminatory outcomes rooted in opaque data pipelines. Messaging platforms face scrutiny over whether “product improvement” language in their terms of service truly allows the use of private communications to train AI models. Data protection authorities in Europe are investigating whether social platforms unlawfully repurposed users’ posts and behavioral data to train conversational agents without a sufficient legal basis. In some cases, companies have paused model training or changed their opt-out flows in response to regulatory pressure, underscoring how fragile public trust can be even when formal legal requirements are technically met.

At the same time, governments in fast-growing digital markets are under pressure from industry to carve out exceptions for AI training under new data protection laws, arguing that strict enforcement could stifle domestic AI ecosystems. These debates preview a broader global struggle: should AI developers have a special license to reuse personal and public data because of the technology’s perceived benefits, or should existing privacy norms and consent rules prevail, even if that slows development and raises costs?

In response, technical privacy strategies are starting to catch up to the legal ones. Privacy-preserving machine learning techniques—federated learning, differential privacy, secure enclaves, homomorphic encryption—are gaining real-world traction, especially in finance, healthcare, and sensitive government workloads. Model architectures and deployment strategies are being revised so that more computation happens near where data resides, reducing the need to centralize raw personal information. Meanwhile, enterprises are realizing that their biggest privacy risk may be internal: employees pasting personally identifiable information, trade secrets, and regulated data into public chatbots, or building internal tools that quietly warehouse prompts for reuse. Shadow AI again becomes a privacy problem as much as a security one, driving demand for in-house, “walled-garden” models, strict data retention controls, and privacy reviews embedded into the AI lifecycle.

Looking ahead, organizations should assume that almost every major privacy and data protection law—from Europe’s GDPR to California’s CCPA and newer regimes in Asia, Latin America, and Africa—will be interpreted through an “AI lens.” Duties to inform, to minimize data, to enable access and deletion rights, and to avoid automated decisions with unjustified discriminatory impact will increasingly be tested against AI systems, not just traditional databases. Companies that treat model training and inference pipelines as black boxes, separate from their privacy programs, are likely to find themselves in regulators’ crosshairs and in the headlines.

From optional to existential

By 2028, AI risk management is likely to look less like a specialized niche and more like a core component of enterprise governance, alongside financial reporting and cybersecurity. Emerging AI risk frameworks emphasize that trustworthiness requires systems to be safe, secure, reliable, privacy-enhanced, transparent, and fair, with trade-offs made deliberately and documented in context. The EU’s AI Act, state-level AI safety laws, and new rules in other major markets will convert many of those aspirations into obligations. At the same time, attackers will be probing every layer of the AI stack, from data collection and training pipelines to deployment and user prompts, looking for ways to turn models against their creators.

For organizations, the next chapter is not about choosing between innovation and restraint. It is about building the muscle to innovate safely: inventorying where AI is used, establishing clear lines of accountability, embedding security and privacy reviews into the AI lifecycle, and preparing to explain—and defend—how these systems work when something goes wrong. AI security, safety, and privacy are no longer edge topics discussed by specialists at niche conferences. They are the terms on which society will decide whether AI becomes the infrastructure we rely on, or the next systemic risk we scramble to contain.

References

• NIST – AI Risk Management Framework (AI RMF 1.0)
  Main framework landing page (official NIST resource):
  https://www.nist.gov/itl/ai-risk-management-framework NIST
  Direct PDF of AI RMF 1.0 (if you prefer citing the document itself):
  https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf NIST Publications

• European Union – Artificial Intelligence Act (Regulation (EU) 2024/1689)
  Official EUR-Lex entry (ELI page) for the AI Act:
  https://eur-lex.europa.eu/eli/reg/2024/1689/oj/eng EUR-Lex
  Direct PDF of the regulation in the Official Journal:
  https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=OJ:L_2024_1689 EUR-Lex

• ENISA – Cybersecurity of AI and Machine Learning / Threat Landscape
  Closest official ENISA publication matching the title and scope you referenced (“AI cybersecurity ecosystem and Threat Landscape”):
  https://www.enisa.europa.eu/publications/artificial-intelligence-cybersecurity-challenges ENISA

• U.S. Congressional Research Service – Artificial Intelligence overview & policy issues
  CRS report on AI with overview, federal activity and selected policy issues (via Congress.gov / CRS product page):
  https://www.congress.gov/crs-product/R47644 Congress.gov

• Judicial & regulatory materials on generative AI training, copyright & data protection
  Because this reference is inherently plural, here are authoritative URLs you can cite as exemplars in that category:

  • U.S. Copyright Office AI hub (landing page linking to all AI reports):
    https://www.copyright.gov/ai/ U.S. Copyright Office

  • U.S. Copyright Office, Copyright and Artificial Intelligence – Part 2: Copyrightability (report PDF):
    https://www.copyright.gov/ai/Copyright-and-Artificial-Intelligence-Part-2-Copyrightability-Report.pdf U.S. Copyright Office+1

  • U.S. Copyright Office, Copyright and Artificial Intelligence – Part 3: Generative AI Training (pre-publication report PDF):
    https://www.copyright.gov/ai/Copyright-and-Artificial-Intelligence-Part-3-Generative-AI-Training-Report-Pre-Publication-Version.pdf U.S. Copyright Office

  • Example GDPR enforcement action related to generative AI (Italian DPA – Garante – fining OpenAI over ChatGPT):
    https://www.garanteprivacy.it/home/docweb/-/docweb-display/docweb/10085432 Garante Privacy+1

#AI security, #AI safety, #AI privacy, #adversarial machine learning, #AI cyberattacks, #shadow AI, #AI governance, #AI regulation, #zero-trust architecture, #AI risk management

Author: Serge Boudreaux – AI Hardware Technologies, Montreal, Quebec
Co-Editor: Peter Jonathan Wilcheck – Miami, Florida