Artificial intelligence is redefining cybersecurity in ways that are powerful, disruptive, and more than a little unsettling. It has become one of the most effective tools for detecting vulnerabilities and threats, making sense of massive data streams​ ​and surfacing patterns that would otherwise go unnoticed. But AI also introduces an entirely new class of risks, making it both ​a​​ ​solution and ​a​​ ​problem. That ​duality ​​ i​s forcing a fundamental rethinking of how security gets designed and maintained across the technology landscape.

At Adeia, this reality shapes how we approach security: it must be built in from the very beginning, starting with the data itself, extending through AI systems, and reaching into future-ready cryptographic frameworks. Across the industry, organizations are weaving stronger protections into every layer, from hardware-based trust anchors and hardened network protocols to zero-trust architectures that eliminate implicit trust.

A Much Bigger Attack Surface

AI changes what needs to be secured, and it also draws a line between two things that often get conflated. AI safety is about preventing harmful outcomes from model behavior, think hallucinations, bias, or unreliable outputs. AI security is about protecting AI systems from adversarial attack: manipulation, data leakage, and system compromise. Both matter, and neither can be solved in isolation.

The attack surface of an AI system spans training data, prompts, retrieval mechanisms, vector databases, external tools, and APIs. Each is a potential entry point. The OWASP Top 10 for LLM Applications catalogues risks unique to AI, including prompt injection, data poisoning, supply chain vulnerabilities, and excessive autonomy. These are not upgraded versions of familiar threats. They exploit the probabilistic nature of AI systems in ways that conventional security tooling was never designed to catch. Recent demonstrations of advanced AI systems, such as Anthropic’s Mythos research, autonomously discovering exploitable software vulnerabilities suggest a future in which attackers can scale sophisticated cyber operations far beyond human capability, dramatically increasing both the speed and volume of emerging threats (https://red.anthropic.com/2026/mythos-preview).

The model supply chain is especially exposed. Pretrained models from external sources, fine-tuning datasets of uncertain provenance, and third-party dependencies each carry risk. What makes this hard is that these threats are encoded in model behavior rather than explicit code, making them far more difficult to detect than traditional software vulnerabilities.

Real Attacks, Right Now

These risks are not theoretical. EchoLeak was a zero-click prompt injection exploit in a production AI system where a crafted email containing hidden instructions caused the AI to encode sensitive data into a link that was automatically retrieved, exfiltrating data with no user interaction and bypassing multiple layers of traditional safeguards.

Attackers are not just exploiting AI. They are deploying it. Researchers have also demonstrated how easily AI systems can be manipulated through retrieval poisoning attacks, where a fake website and even a single malicious Wikipedia edit were enough to cause multiple large language models to confidently repeat fabricated information as fact, highlighting how fragile AI trust and source validation mechanisms can be in practice (https://www.theregister.com/software/2026/04/29/fooling-large-language-models-just-keeps-getting-simpler/5229286?utm_source=chatgpt.com). In one of the first reported large-scale AI-driven espionage campaigns, detailed in an Anthropic report, autonomous agents conducted reconnaissance, discovered vulnerabilities, executed exploits, and moved laterally across systems, handling 80 to 90 percent of the operational work. The only thing that kept humans in the loop was the AI's own hallucinations. From a defender's perspective, that is a strange silver lining: in this case, hallucinations were the feature, not the bug.

This points to a two-sided threat model. On one side are attacks on AI systems: prompt injection, model extraction, and data poisoning. On the other are attacks by AI systems: convincing phishing campaigns, automated malware development, and social engineering at scale. AI-powered attacks are not just more efficient. They continuously adapt, making them harder to detect.

Agents, Identity, and Data

The OWASP Top 10 for Agentic Applications captures a newer layer of risk. Autonomous AI agents that plan multi-step tasks and interact with external tools can have their goals manipulated, their memory poisoned, or their privileges abused, sometimes operating entirely outside their intended boundaries. These risks live in the behavior and decision-making of intelligent systems, not just in the code.

As agents become more common, identity is becoming the central control layer for AI security. Every action an AI system takes needs to be governed by strong identity controls, clear attribution, and least-privilege enforcement, covering not just human users but machine identities for models, agents, and tools. Perimeter-based security simply does not hold in this environment.

Underneath everything is data. AI systems do not just query data; they learn from it, embedding it into model behavior in ways that are hard to inspect. This creates a circular dependency: AI is increasingly used to improve security, but its effectiveness depends on the quality and integrity of the data it was trained on. Reliable data pipelines, unified visibility, and cryptographic techniques for traceability are no longer optional.

What Good Security Looks Like Now

Securing AI requires designing protection into the entire lifecycle, from data collection and training through deployment and runtime. That means input validation, prompt isolation, output filtering, continuous monitoring, red teaming, and rigorous identity and access management. For agentic systems, add constrained autonomy, human oversight, and runtime policy enforcement. AI firewalls are also gaining traction as a way to provide real-time inspection over prompts, outputs, and system behavior.

One persistent gap is the lack of standardized evaluation methods. AI systems behave probabilistically and fail in unpredictable ways that existing benchmarks do not capture. Without agreed-upon frameworks for measuring security robustness, organizations are left with inconsistent, ad hoc testing and results that are hard to compare or act on.

There are also real tradeoffs to acknowledge. Safeguards add latency and cost. In competitive environments, many organizations consciously relax certain controls for the sake of speed. Managing that tension well requires clear, deliberate frameworks for risk-based decision-making rather than quiet defaults.

The Road Ahead

Quantum computing adds another layer of urgency. Attackers may already be harvesting encrypted data today, planning to decrypt it once quantum capability matures. The transition to post-quantum cryptography cannot be deferred.

The bottom line is this: security now means ensuring trust in intelligent, adaptive, and increasingly autonomous technologies. That requires integrating data security, AI safety, and AI security into a unified strategy rather than treating them as separate workstreams. Organizations that move on this will be better positioned for what comes next. Those that do not face a widening gap in a world where both defenders and attackers are increasingly powered by the same technology.